WO2018014764A1 - 一种商品对象选取、模型确定及使用热度确定方法与装置 - Google Patents
一种商品对象选取、模型确定及使用热度确定方法与装置 Download PDFInfo
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Definitions
- the present application relates to the field of Internet technologies, and in particular, to a commodity object selection, model determination, and usage heat determination method and apparatus.
- the e-commerce system In order to improve the transaction performance of commodity objects in the e-commerce system, the e-commerce system often establishes various new channels to increase the exposure of the product objects, for example, establishing various special spike activity theme channels, or the main product object tonality.
- Theme channels and more.
- the embodiment of the present application provides a method and device for selecting a product object, determining a model, and determining a usage heat, so as to solve the problem of inefficiency existing in the existing product object selection mode.
- the embodiment of the present application provides a method for selecting a commodity object, including:
- the object in the second time period uses an identification model of the relationship between the heats; the first time period is a previous specified time period of the second time period;
- selecting at least one commodity object from the primary selection object as the commodity object in the second time period that matches the commodity object keyword includes:
- Selecting at least one object from the primary selection object uses a commodity object having a heat not lower than a set heat threshold as a commodity object in the second time period that matches the commodity object keyword.
- the method before determining, according to the set object identification model and the use feature data of each of the preliminary objects in the first time period, before the object usage heat of each of the preliminary objects in the second time period, the method further includes:
- the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes Using the feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time period is the corresponding fourth The previous specified time period of the time period;
- the initial object recognition model is trained to obtain the object recognition model.
- the method further includes:
- each primary selection object according to the set category identification model and the category corresponding to each primary selection object in the category usage heat of one or more historical synchronization time periods corresponding to the second time period
- the category uses heat in the category of the second time period; wherein the category recognition model is trained Between the use of the category of the commodity object category in the second time period, and the heat usage of the category of the commodity object category in the one or more historical time periods corresponding to the second time period Identification model of the relationship;
- the hotspot of the second time period is used to update the object usage heat in the second time period according to the category corresponding to the primary selection object.
- the method further includes:
- the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each category identifies the model sample object basis
- the feature data includes the category usage heat of the category corresponding to the category identification model sample object in each fifth time period, and the category corresponding to the category identification model sample object corresponds to each fifth time period.
- the pre-established use of the category for predicting the commodity object category in the second time period is used, and the commodity object category corresponds to the second time period.
- the category of one or more historical contemporaneous time periods is trained using an initial category recognition model of the relationship between the heats to obtain the category identification model.
- the historical time period of each time period refers to a historical time period that is on the same calendar day or lunar day as the time period and corresponds to the time period.
- the object in the second time period is used in the category according to the category corresponding to the primary selection object, and the object of the primary selection object is in the second time period.
- the method further includes:
- the category corresponding to the primary selection object is a category that matches a specific time period corresponding to the second time period, according to the set coefficient And increasing the category usage heat of the category corresponding to the primary selection object in the second time period.
- At least one commodity object is selected from the preliminary objects as the second time period, Before the commodity object matching the commodity object keyword, the method further includes:
- the method before acquiring the primary selected object that matches the commodity object keyword, the method further includes:
- the determined sample words are used as the final desired commodity object keywords.
- the object recognition model is a regression model; the category recognition model is a linear model.
- the embodiment of the present application provides another method for selecting a commodity object, including:
- the product object information is used by the server according to the set object recognition model and the primary selection objects that match the product object topic in the first time period, and the product data from the product.
- the set object recognition model is used to characterize the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period.
- the method before receiving the product object keyword input by the user, the method further includes:
- the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object recognition
- the base feature data of the different model sample object includes the use feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period;
- the third time period is a previous specified time period of the corresponding fourth time period;
- Transmitting the object recognition model training sample data to a server wherein the server identifies the basic feature data of each object recognition model sample object included in the sample data according to the object recognition model, and the pre-established product object for predicting The initial object recognition model using the relationship between the feature data and the object use heat in the second time period in the first time period is trained to obtain the set object recognition model.
- the embodiment of the present application provides a method for determining a model, including:
- the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes Using the feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time period is the corresponding fourth The previous specified time period of the time period;
- the pre-established use feature data for predicting the product object in the first time period, and the commodity object in the second time is trained using the initial object recognition model of the association relationship between the heats, and is obtained between the use feature data for characterizing the commodity object in the first time period and the object use heat of the commodity object in the second time period.
- An object recognition model of the association relationship; the first time period is a previous specified time period of the second time period.
- the usage characteristic data includes at least one or more of the number of browsing times, the number of collections, the number of purchases, the number of transactions, the number of comments, and the number of searches; the usage heat of the object includes at least volume, turnover, And any one or more of the transaction conversion rates.
- the embodiment of the present application provides another method for determining a model, including:
- the type training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category corresponding to the category identification model sample object at each first time
- the category of the segment uses the heat, and the category corresponding to the category identification model sample object uses the heat in the category of one or more historical time periods corresponding to the respective first time periods;
- the category is trained in an initial category recognition model of the association relationship between the categories of heat usage using one or more historical time periods corresponding to the second time period, and is obtained in the second category for characterizing the commodity object.
- the category of the time period uses a heat classification, a category recognition model that is related to the heat usage of the category of the commodity object category in one or more historical time periods corresponding to the second time period.
- the heat usage of the category includes at least one or more of a volume, a turnover, and a transaction conversion rate.
- the embodiment of the present application further provides a method for determining a heat usage, including:
- each item based on the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and the use feature data of each product object in the first time period
- the object uses heat in the second time period
- the first time period is a previous specified time period of the second time period; and the association relationship is based on usage characteristic data of each sample object in each third time period and each sample object is in each corresponding fourth time
- the object of the segment is established using the heat;
- the third time period is the previous specified time period of the corresponding fourth time period.
- the embodiment of the present application further provides a commodity object selection device, including:
- a keyword receiving unit configured to receive a commodity object keyword sent by the user terminal
- An object obtaining unit configured to acquire a primary selection object that matches the keyword of the commodity object
- a heat determining unit for identifying a model based on the set object and each of the primary objects at the first time Using the feature data in the segment, determining the object usage heat of each primary object in the second time period; wherein the object recognition model is the training use characteristic data used to represent the commodity object in the first time period, and a recognition model of an association relationship between the objects of the commodity object in the second time period; the first time period is a previous specified time period of the second time period;
- An object screening unit configured to select at least one commodity object from the primary selection object as the second time period according to the object usage heat of each primary selection object in the second time period, and the subject matter of the commodity object Matching item object.
- the embodiment of the present application further provides another commodity object selection device, including:
- a keyword receiving unit configured to receive a commodity object keyword input by the user
- a keyword sending unit configured to send the commodity object keyword to a server
- An object information receiving unit configured to receive commodity object information returned by the server according to the commodity object keyword
- An object determining unit configured to determine a product object corresponding to the product object information as a product object that matches the product object keyword in a second time period
- the product object information is used by the server according to the set object recognition model and the primary selection objects that match the product object topic in the first time period, and the product data from the product.
- the set object recognition model is used to characterize the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period.
- the embodiment of the present application further provides a model determining apparatus, including:
- a data receiving unit configured to receive object recognition model training sample data sent by the user terminal, where the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object recognition model sample object
- the basic feature data includes the use feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time The segment is the corresponding fourth time The previous specified time period of the segment;
- a model training unit configured to perform, according to the basic feature data of each object recognition model sample object included in the sample identification model training sample data, the pre-established use characteristic data for predicting the commodity object in the first time period, and
- the commodity object is trained in the initial object recognition model of the relationship between the objects using the heat in the second time period, and the use feature data for characterizing the commodity object in the first time period and the second time period of the commodity object are obtained.
- An object recognition model in which an object uses an association relationship between heats.
- the embodiment of the present application further provides another model determining apparatus, including:
- a data receiving unit configured to receive the category identification model training sample data sent by the user terminal, wherein the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each category Identifying the basic feature data of the model sample object includes the category usage heat of the category corresponding to the category identification model sample object in each first time period, and the category corresponding to the category identification model sample object in each category The usage of the category of one or more historical time periods corresponding to a period of time;
- a model training unit configured to: according to the category identification model, the basic feature data of the sample objects of the various types of mesh recognition models included in the sample data, and the pre-established categories for predicting the category of the commodity object in the second time period Training is performed using the initial category recognition model of the relationship between the heat and the category of the commodity object category in the one or more historical time periods corresponding to the second time period, and is used to characterize the commodity.
- the category of the object category in the second time period is the category of the association between the heat usage and the category usage heat of the commodity object category in one or more historical time periods corresponding to the second time period. Identify the model.
- the embodiment of the present application further provides a heat determining device, including:
- a data obtaining unit configured to acquire usage characteristic data of each commodity object in a first time period
- a heat determining unit configured to use an association relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and use of each commodity object in the first time period Feature data, determining the object usage heat of each commodity object in the second time period;
- the first time period is a previous specified time period of the second time period; and the association relationship is based on usage characteristic data of each sample object in each third time period and each sample object is in each corresponding fourth time
- the object of the segment is established using the heat;
- the third time period is the previous specified time period of the corresponding fourth time period.
- the embodiment of the present application provides a method and device for selecting a product object, determining a model, and determining a usage heat, and automatically selecting at least one product object from a mass object based on a product object keyword input by the user and a set object recognition model.
- FIG. 1 is a schematic diagram of a possible application scenario of a method for selecting a commodity object according to Embodiment 1 of the present application;
- FIG. 2 is a schematic flowchart diagram of a method for selecting a commodity object in the first embodiment of the present application
- FIG. 3 is a schematic flowchart diagram of a method for determining a model in the first embodiment of the present application
- FIG. 4 is a schematic flowchart diagram of another method for determining a model in the first embodiment of the present application
- FIG. 5 is a schematic flowchart diagram of a method for determining a heat usage according to Embodiment 1 of the present application
- FIG. 6 is a schematic diagram showing a possible structure of a product object selection device in Embodiment 2 of the present application.
- FIG. 7 is a schematic diagram showing a possible structure of another commodity object selection device in the second embodiment of the present application.
- FIG. 8 is a schematic structural diagram of a model determining apparatus in Embodiment 2 of the present application.
- FIG. 9 is a schematic diagram showing a possible structure of another model determining apparatus in Embodiment 2 of the present application.
- FIG. 10 is a schematic diagram showing a possible structure of a heat determining device according to Embodiment 2 of the present application.
- Embodiment 1 is a diagrammatic representation of Embodiment 1:
- FIG. 1 A schematic diagram of an application scenario, which may include, for example, a user terminal 11 and a server 12, where:
- the user terminal 11 can receive the product object keyword input by the user 10 and send the product object topic word to the server 12; the server 12 can obtain the product object subject word according to the product object keyword sent by the user terminal 11 Matching the primary selection object, and based on the set object recognition model and the usage characteristic data of each primary selection object in the first time period, determining the object usage heat of each primary selection object in the second time period, and according to each primary selection
- the object uses the heat in the second time period, and selects at least one product object from the primary selection object as the commodity object in the second time period that matches the product object keyword; the user terminal 11 can receive The product object information of the at least one product object returned by the server 12 after selecting at least one product object, and the product object information Corresponding commodity object as a commodity object in the second time period that matches the commodity object keyword; wherein the set object recognition model can be used to characterize the use of the commodity object in the first time period The relationship between the feature data and the object usage heat of the commodity object in the second time period; the first time period is
- the user terminal 11 and the server 12 can perform a communication connection through a communication network, and the network can be a local area network, a wide area network, or the like.
- the user terminal 11 may be a terminal device such as a mobile phone, a tablet computer, a notebook computer, a personal computer, or even a client installed in the terminal device;
- the server 12 may be any server device capable of supporting processing operations such as screening of commodity objects. .
- At least one product object can be automatically selected from the mass object as the final object satisfying the user's demand based on the product object keyword input by the user and the set object recognition model, thereby The efficiency of selecting commodity objects is greatly improved, thereby reducing the cost of manual inventory and improving operational efficiency.
- the method for selecting a product object may include the following steps:
- Step 201 The user terminal receives the object recognition model training sample data input by the user, and sends the object recognition model training sample data to the server.
- the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes the object recognition model sample object in each third time period.
- the use feature data within, and the object recognition model sample object uses heat in each corresponding fourth time period; the third time period is a previous specified time period of the corresponding fourth time period.
- the usage feature data of the commodity object such as each object recognition model sample object may include at least Any one or more of the number of times of browsing, the number of times of collection, the number of purchases (adding to the shopping cart), the number of transactions, the number of comments, and the number of searches;
- the object usage heat of each object object such as the object recognition model sample object may include at least a deal Any one or more of quantity, turnover, and transaction conversion rate.
- the use feature data of the product object such as each object recognition model sample object can be acquired from the product object operation information of each e-commerce website, and the object usage heat of each product object such as the object recognition model sample object can be based on each The feature data of the product object such as the object recognition model sample object is calculated and will not be described here.
- third and fourth time periods may generally be historical time segments, that is, the basic feature data of each object recognition model sample object may generally be corresponding historical data; and, third, fourth The time period, and the time period of the specified time period can be flexibly set according to the actual situation, such as 1 day, 1 week, 1 month, etc. (usually a minimum of one day).
- the lengths of the third and fourth time periods may be the same or different, for example, the fourth time period may be one day, and the third time period corresponding to the fourth time period (ie, the previous designation of the fourth time period)
- the time period may be one month or one year, etc., or the fourth time period may be one month, and the third time period corresponding to the fourth time period may be one day, etc.;
- the three time periods may be the previous specified time period adjacent to the fourth time period, and may or may not be adjacent.
- Step 202 The server receives the object recognition model training sample data sent by the user terminal, and according to the object recognition model, the basic feature data of each object recognition model sample object in the sample data is trained, and the pre-established initial object recognition model is trained to obtain the The required object recognition model.
- the initial object recognition model is a recognition model for predicting an association relationship between the use feature data of the commodity object in the first time period and the object use heat of the commodity object in the second time period; the object recognition The model is a training model for characterizing the relationship between the use feature data of the commodity object in the first time period and the heat usage of the object object in the second time period, the first time period is The previous specified time period of the second time period.
- the sizes of the first and second time periods and the like may also be flexibly set according to actual conditions, and the lengths of the first and second time periods may be different. The same may be different (however, the size of the first time period may be the same as the third time period, and the size of the second time period may be the same as the fourth time period), which is not limited thereto.
- the object usage identification object model object and the object object use heat as the transaction volume, for example, the corresponding object recognition model can be obtained by the following steps:
- A1 Establish an initial object recognition model related to volume.
- the third and fourth time periods can be set to 1 day; and it is assumed that y(t) can represent the volume of a certain commodity object on the date t, and the commodity object is represented by x1(t-1)
- x1(t-1) The number of times of the date t-1, x2 (t-1) indicates the number of times the item is stored on the date t-1, and x3(t-1) indicates the number of times the item is purchased on the date t-1, etc.
- A2 Calculating the transaction volume of each object recognition model sample object in each fourth time period (such as the daily sales volume at the date t), and using the feature data of the third time period corresponding to each object recognition model sample object ( For example, the feature data such as browsing, collection, purchase, transaction, comment, search, etc. of the date t-1 are associated to obtain a plurality of associated data; and based on the obtained associated data, the established initial object recognition model is trained to obtain The actual values of the coefficients a(1), a(2), a(3), etc., to obtain the desired object recognition model.
- the regression model can better cross the features, improve the predictive ability, and prevent the merchant from cheating, thereby improving the accuracy of the prediction.
- the initial object recognition model and the object recognition model may generally be regression models such as a Gradient Boost Regression Tree model.
- the initial object recognition model and the object recognition model may also adopt a linear model, which is not limited herein.
- the object recognition model may be updated in real time or periodically according to the latest object recognition model sample data to improve the accuracy of the object recognition model.
- the use feature data of each object recognition model sample object in each third time period, and the object use heat of each object recognition model sample object in each corresponding fourth time period may be replaced with each object recognition.
- the corresponding usage feature data of the model sample object under the channel and the object usage heat and the like are used to better predict the object usage heat of the product object under the channel, and details are not described herein again.
- steps 201 and 202 are steps of establishing an object recognition model in advance, and are not required to be performed every time the product object is selected, unless the object recognition model trains sample data. A corresponding update has occurred. That is, after performing step 201 and step 202, the subsequent steps may be repeatedly executed multiple times, and details are not described herein.
- Step 203 The user terminal receives the product object keyword input by the user, and sends the product object keyword to the server.
- the user terminal may further extend the received product object keyword to the server, and may expand the received product object keyword and The expanded product object subject is sent to the server to increase the richness of the product object keywords.
- the user terminal expands the product object keyword input by the user, and can also avoid the situation that the server needs to simultaneously expand the received large number of product object keywords when a large number of user terminals simultaneously send the product object keyword to the server. Occurs to save the processing resources of the server and reduce the working pressure of the server, thereby further improving the speed and efficiency of subsequent product object selection.
- the user terminal may augment the received product object keyword in the following manner:
- the set sample corpus may be a corpus of e-commerce news and the like crawled from an external website by a crawler; and, in addition, based on the set sample corpus, determine a phase with each product object keyword
- the language model capable of characterizing the word as a real-value vector such as the word2vec model
- the word2vec model may be firstly trained based on the set sample corpus, and based on the trained word2vec
- a language model such as a model converts each product object keyword input by the user and each word in the sample corpus into a vector; after that, the calculated similarity calculation formula, such as the Cosine formula, can be used to calculate each word in the sample corpus.
- the similarity between the keyword objects of each product object input by the user; finally, the word above the value (which may include the value) is selected as the final desired topic word by setting a corresponding similarity threshold.
- the user terminal may perform the three words based on the set sample corpus. Expansion, such as expansion to get “fashion”, “trend”, “shirt”, “suit”, “dress”, “jeans” and other words, and the expanded words as the final product object keywords.
- Step 204 The server receives the commodity object keyword sent by the user terminal, and acquires a primary selection object that matches the commodity object keyword.
- the server may search for the corresponding product object title from the product object information of each e-commerce website according to the product object keyword transmitted by the user terminal, and match the product object keyword sent by the user terminal.
- a product object (such as partial matching, etc.), and each of the searched product objects is a preliminary selection object that matches the product object keyword transmitted by the user terminal.
- Each of the product object information in the e-commerce website may include basic information such as an ID (identification), a name (ie, a title), a place of origin, a seller user information, and a category of the product object, and details are not described herein.
- the server may further augment the received product object keyword before acquiring the primary object that matches the product object keyword according to the product object keyword sent by the received user terminal. In order to obtain the corresponding primary selection object based on the expanded commodity object subject terms.
- the specific implementation manner in which the server augments the received product object keyword is similar to the specific implementation manner in which the user terminal expands the received product object keyword in step 203, and details are not described herein.
- the product object theme input to the user is executed by the server instead of the user terminal.
- the operation of the word expansion can reduce the performance requirements of the user terminal, so that the method described in the embodiment of the present application has a wider scope of application; in addition, for the user terminal, the processing resources of the user terminal can be saved, and the user terminal can be saved. Work pressure.
- Step 205 The server determines, according to the object recognition model obtained by the training and the use feature data of each primary selection object in the first time period, the object usage heat of each primary selection object in the second time period.
- the object recognition model is that the server recognizes the volume of the model sample object on each date according to each object, and the browsing, collection, and purchase of each object recognition model sample object on one or more dates before the corresponding date.
- the object recognition model trained by the feature data such as transaction, comment, search, etc. may be based on the object recognition model, and browse, collect, purchase, and trade according to one or more dates of each primary object before the date t+1.
- Characteristic data such as comments, searches, etc., predict the volume of each primary object at the date t+1.
- the prediction may not be well predicted, and therefore, the transaction amount may not be directly predicted.
- the volume of each commodity object is predicted, and then the transaction amount is multiplied by the corresponding price to improve the accuracy of the prediction.
- Step 206 The server selects at least one commodity object from the primary selection objects according to the object usage heat of each primary selection object in the second time period, and matches the keyword of the commodity object in the second time period.
- the server may select at least one object from the primary selection object according to the heat usage of the objects in the second time period of each primary selection object, and the usage heat is not lower than the setting.
- the product object of the heat threshold (which can be flexibly set according to actual conditions) is used as the product object in the second time period that matches the keyword of the product object.
- the server may also sort the primary objects according to the order in which the object usage heat is used, and take the pre-K (K is any positive integer) primary selection objects as the second time period.
- K is any positive integer
- the primary screening object may be manually filtered according to actual needs, or the short-term object is not used hotly. Or a product object whose price does not meet the user's needs (for example, a commodity object with only 5 transactions within three days and a price between 10 and 200 yuan), so as to select a desired commodity object based on the selected primary objects; and / or,
- the selected commodity object may be manually screened according to actual needs, or Deleting the short-term object using the commodity object whose heat is not high or the price does not meet the user's demand, and using the filtered commodity object as the final desired commodity object in the second time period and matching the commodity object keyword I will not repeat them.
- the object usage heat of each primary selection object may be adjusted according to the time information, thereby adjusting the order of each primary selection object.
- the time series model can be used to predict the heat of the category in the second time period, so that some seasonal commodity objects can emerge in advance to further improve the accuracy of the selection of the commodity object.
- the method may further include:
- each primary selection object according to the set category identification model and the category corresponding to each primary selection object in the category usage heat of one or more historical synchronization time periods corresponding to the second time period
- the category uses heat in the category of the second time period; wherein the category identification model is trained to represent the category of the commodity object category in the second time period using the heat, and the commodity object category Between the heat usage of the category of one or more historical time periods corresponding to the second time period Identification model of association relationship;
- the hotspot of the second time period is used to update the object usage heat in the second time period according to the category corresponding to the primary selection object.
- the category corresponding to the primary selection object may be in the category of the second time period and the category of the primary selected object in the second time period.
- the updated object in the second time period as the primary selection object uses the heat.
- the heat usage of the object is similar to the heat usage of the object, and the heat usage of the category may include at least one or more of a volume, a turnover, and a transaction conversion rate, and the category of the commodity object such as each sample object is used. It can be calculated based on the use characteristic data of the product object such as each sample object, and is not limited thereto.
- the historical time period of each time period refers to a historical time period that is on the same calendar day or lunar day as the time period and corresponds to the time period; for example, for the time period January 01, 2016 ⁇
- the historical period of the period can be from January 01, 2015 to January 05, 2015, January 01, 2014 to January 05, 2014, etc. This is not to be repeated.
- the category based on the set category identification model and the category corresponding to each primary selection object is one or more historical synchronization time periods corresponding to the second time period.
- the server can obtain the category identification model by:
- the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each category identifies the model sample object basis
- the feature data includes the category usage heat of the category corresponding to the category identification model sample object in each fifth time period, and the category corresponding to the category identification model sample object corresponds to each fifth time period.
- the pre-established use of the category for predicting the commodity object category in the second time period, and the commodity object category and the second time period is trained using an initial category recognition model of the relationship between the heats to obtain the category identification model.
- the fifth time period may be a historical time period; and, similar to the foregoing descriptions regarding the first, second, third, and fourth time periods, the size of the fifth time period may also be based on The actual situation is flexible (although the size of the fifth time period is usually the same as the second time period), which is not limited.
- the corresponding category identification model can be obtained by the following steps:
- B1 Establish an initial category recognition model related to volume.
- each fifth time period can be set to 1 month; the initial category recognition model is a linear model; and it is assumed that the volume of a certain category in this year is z(t), and the volume of the category in the same period last year.
- the linear model is used as the category recognition model because the model parameters are small and the historical data of the category is relatively stable.
- other models such as a regression model, may be used as the category recognition model to improve the accuracy of the heat prediction using the category, which is not limited herein.
- B2 Use the historical data of the category of the latest period of time (such as the latest 3 months of the category history volume of this year) and its corresponding historical data (as in the same period of at least two years on a calendar or lunar calendar) Historical data), the established initial category recognition model is trained to obtain the actual values of the coefficients b(1), b(2), etc., to obtain the final category recognition model.
- the category identification model may be updated in real time or periodically according to the latest category identification model sample data to improve the category recognition model.
- Accuracy again, taking a product object as an example, after the channel is online, it can also use the heat of the categories of the various target recognition model sample objects in each fifth time period, and various types of object recognition.
- the model sample object is replaced with a category of the target recognition model sample object under the channel in one or more historical synchronization time periods corresponding to each fifth time period.
- the corresponding categories use heat, etc., to better predict the heat usage of the category of the product object category under the channel, and will not be described here.
- the category identification model obtained by the training and the class corresponding to each primary selection object may be Determining the category corresponding to each primary object by using the heat of the category in the one or more historical time periods corresponding to the next month (for example, the historical time period of the previous year or the previous two years) The heat is used in the category of the next month.
- the corresponding initial category recognition model may be established in units of days, such as establishing the following initial class.
- the recognition model z1(t) b1(1)*z1(t-1)+b1(2)*z1(t-2)+..., where z1(t) is a category of this year date t
- the heat used for the purpose, z1(t-1) is the heat used for the category of the same category last year, z1(t-2) is the heat used for the category of the same period of the previous year, and so on; b1(1), b1 (2) Wait for the coefficient to be estimated; after that, use the historical data of the latest period of time (such as 90 days, etc.) and its corresponding historical data to train the established initial category recognition model to obtain b1.
- the values of the coefficients (1) and b1(2) are used to obtain the final category identification model. After that, the category identification model obtained by the training is used to calculate the 30-day category of each category. The heat, then, the average is used to obtain a more stable category of heat usage for each month, and will not be repeated here.
- the second time period when the second time period is determined to be a specific time period such as a holiday, the second time period may be corresponding to
- the various categories of destinations associated with a particular time period are additionally weighted using heat (the degree of additional weighting can be based on actual demand) to ensure that commodity objects corresponding to these categories can emerge in time. For example, the Mid-Autumn Festival moon cake will be hot, and thus, when the second time period is the Mid-Autumn Festival time period, the weight category corresponding to the moon cake can be additionally weighted.
- the method may further include:
- the second time period is a specific time period (such as a Mid-Autumn Festival, a Dragon Boat Festival, and the like, a holiday time period, etc.)
- the category corresponding to the primary selection object is a specific time corresponding to the second time period
- the category matched by the segment is based on the set coefficient (the coefficient can be flexibly adjusted according to the actual situation. For example, if the degree of matching between the category and the specific time period is higher, the coefficient can be larger, if the matching degree is lower, Then, the coefficient may be smaller, etc., and the category corresponding to the primary selection object is used to increase the heat usage of the category in the second time period.
- each commodity object may result in the appearance of some homogeneous commodity objects, such as "Mid-Autumn Festival Gifts Italian Imports Ferrero Chocolate Roses DIY Gift Boxes Birthday Lovers” and "SF” Italian Ferrero chocolate DIY heart-shaped rose gift box Mid-Autumn Festival birthday gift”
- some homogeneous commodity objects such as "Mid-Autumn Festival Gifts Italian Imports Ferrero Chocolate Roses DIY Gift Boxes Birthday Lovers” and "SF” Italian Ferrero chocolate DIY heart-shaped rose gift box Mid-Autumn Festival birthday gift”
- the method may further include:
- a collection of objects For each group of at least one primary selection object whose similarity between each other is not lower than a set similarity threshold (the similarity threshold may be the same or different from the first similarity threshold mentioned in the foregoing) a collection of objects, retaining an object in the collection of objects, and deleting other objects, so that at least one commodity object can be selected from each primary selection object obtained after performing the deletion operation as the second time period, The commodity object whose product object keywords match.
- the similarity between the respective product objects can be obtained by calculating the similarity of the product object titles.
- other similar similarity calculation formulas may be used to calculate the similarity between the commodity objects, which is not limited thereto.
- each group of objects consisting of at least one primary selection object whose similarity between them is not lower than a set similarity threshold
- an object in the object set is retained, it is usually retained.
- the corresponding object uses the object with the highest heat to improve the transaction performance of the commodity object and improve the user's application experience.
- the related logic can also be configured so that the product object finally displayed by the channel is not repeated within a few days, here is not Let me repeat.
- each primary selection object in addition to selecting at least one product object from the primary selection object as the commodity object in the second time period that matches the product object keyword, each primary selection object may be performed. In addition to the same operation, the selected ones may be selected in a similar manner after selecting at least one commodity object from the primary selection objects as the commodity objects in the second time period that match the product object keywords.
- the commodity object performs the same operation.
- the server may select at least one product object from the primary selection object as the product object in the second time period that matches the product object keyword, and may further display the product object information of the selected product object (The unique identification information such as the ID of the product object or the link of the product object is stored and/or transmitted to the user terminal.
- Step 207 The user terminal receives the product object information of the product object that is returned by the server and matches the product object keyword, and uses the product object corresponding to the product object information as the second time period, and the The item object whose product object keyword matches.
- the user terminal may display the received product object information, and/or display the product object corresponding to the product object information for the user to view, which is not described herein.
- step 201, step 203, and step 207 independently constitute a product object selection process executed on the user terminal side, and step 202, step 204 to step 206 are independent.
- the structure of the product object selection executed on the server side is not described herein.
- the embodiment of the present application further provides two methods for determining a model.
- the method for determining a model may include the following steps:
- Step 301 Receive object recognition model training sample data sent by the user terminal.
- the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes the object recognition model sample object in each third time period.
- the use feature data within, and the object recognition model sample object uses heat in each corresponding fourth time period; the third time period is a previous specified time period of the corresponding fourth time period.
- Step 302 According to the basic feature data of each object recognition model sample object included in the object recognition model training sample data, the pre-established initial object recognition model is trained to obtain a desired object recognition model.
- the initial object recognition model is configured to predict a relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period; the first time period is The previous specified time period of the second time period.
- the object recognition model is used to characterize the relationship between the use feature data of the commodity object in the first time period and the object use heat of the commodity object in the second time period.
- the another method for determining a model may include the following steps:
- Step 401 Receive the category identification model training sample data sent by the user terminal.
- the category identification model training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category identification model sample object corresponding to the category identification data.
- the category is in each set sample time period (such as the fifth time period in the foregoing product object selection method; in addition, if the aforementioned product object selection method is not considered, the set sample time period may also be expressed as the first time Segment, in the second time period mentioned later with this model determination method.
- the category usage heat which is not described in detail, and the category of the category identification model sample object corresponding to one or more historical synchronization time periods corresponding to each set sample time period Use heat.
- Step 402 Train the basic feature data of the sample objects of the various types of mesh recognition models included in the sample data according to the category identification model, and train the pre-established initial category recognition model to obtain a desired category recognition model.
- the initial category identification model is configured to predict a category usage heat of the commodity object category in the second time period, and one or more historical synchronization time periods corresponding to the commodity object category corresponding to the second time period.
- the category uses the relationship between the heats.
- the category identification model is used to characterize the category usage heat of the commodity object category in the second time period, and the category of one or more historical synchronization time periods corresponding to the commodity object category corresponding to the second time period. Use the relationship between the heats.
- execution bodies of the model determination method shown in FIG. 3 and FIG. 4 may all be servers; and the specific implementation of each step of the model determination method shown in FIG. 3 and FIG. I will not repeat them.
- the usage heat determination method may include the following steps:
- Step 501 Acquire usage characteristic data of each commodity object in a first time period
- Step 502 based on the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and the use feature data of each commodity object in the first time period, Determining the object usage heat of each commodity object in the second time period;
- the first time period is a previous specified time period of the second time period; and the association relationship is based on the use feature data of each sample object in each third time period and each corresponding sample object in each corresponding The object of the four time period is established using the heat; the third time period is the previous specified time period of the corresponding fourth time period.
- association relationship is similar to the set object recognition model described above.
- specific implementation of each step of using the heat determination method shown in FIG. 5 can be referred to the foregoing related description, and no further description is provided herein. .
- At least one product object can be automatically selected from the mass object based on the product object keyword input by the user and the set object recognition model.
- the ultimate object that meets the user's needs thereby greatly improving the efficiency of the selection of commodity objects, thereby reducing the cost of manual inventory and improving operational efficiency.
- the object can be used in the second time period according to each of the primary objects, at least one object selected from the primary objects is used as the final desired product object, and the product object having the heat not lower than the set heat threshold is used as the final desired product object. Can improve the accuracy of the selection of commodity objects.
- the time series model that is, the category identification model
- the heat is used, and the order of each commodity object is adjusted according to the category, so that the season can be Holidays and other timely adjustment of commodity objects to further reduce the cost of manual inventory and improve operational efficiency.
- the solution described in the embodiment of the present application has no language, software or hardware limitation, and can be implemented based on a general cloud computing platform.
- a high-performance programming language such as C, C++, or Java
- high-performance hardware etc., which is not described in this embodiment.
- Embodiment 2 is a diagrammatic representation of Embodiment 1:
- the second embodiment of the present application provides a product object selection device.
- the product object selection device refer to the related description of the server in the first embodiment of the method.
- the details of the product object selection device may include:
- the keyword receiving unit 601 is configured to receive a product object keyword sent by the user terminal;
- the object obtaining unit 602 is configured to obtain a primary selection object that matches the keyword of the product object
- the heat determining unit 603 can be used to identify the model based on the object and each of the primary objects in the first Using the feature data for a period of time, determining the object usage heat of each of the preliminary objects in the second time period; wherein the object recognition model is the trained use characteristic data for characterizing the commodity object in the first time period And a recognition model of an association relationship between the object usage heat of the commodity object in the second time period; the first time period being a previous specified time period of the second time period;
- the object screening unit 604 is configured to select, according to the object usage heat of the first selected object in the second time period, at least one commodity object from the primary selection object as the keyword in the second time period Matching product objects.
- the object screening unit 604 is specifically configured to select at least one object from the primary selection object to use a commodity object whose heat is not lower than a set heat threshold, as the second time period, and the commodity object theme.
- the product object that matches the word is specifically configured to select at least one object from the primary selection object to use a commodity object whose heat is not lower than a set heat threshold, as the second time period, and the commodity object theme. The product object that matches the word.
- the commodity object selection device may further include an object recognition sample data receiving unit 605 and an object recognition model determining unit 606:
- the object identification sample data receiving unit 605 is configured to determine an object of each primary selection object in the second time period based on the set object recognition model and the usage feature data of each primary selection object in the first time period. Before using the heat, receiving the object recognition model training sample data sent by the user terminal, wherein the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object identifies a model sample object basis
- the feature data includes usage feature data of the object recognition model sample object in each third time period, and object usage heat of the object recognition model sample object in each corresponding fourth time period; the third time period is a corresponding specified time period of the corresponding fourth time period;
- the object recognition model determining unit 606 is configured to: according to the basic feature data of each object recognition model sample object, use the pre-established use feature data for predicting the commodity object in the first time period, and the commodity object in the second time
- the object of the segment is trained using an initial object recognition model of the relationship between the heats to obtain the object recognition model.
- the commodity object selection device may further include a category heat determination unit 607 and an object heat update unit 608:
- the category heat determining unit 607 is configured to: before selecting at least one product object from the primary selection object as the commodity object in the second time period that matches the product object keyword, based on the set class Determining the model, and the category corresponding to each primary object in the category of one or more historical time periods corresponding to the second time period, determining the category corresponding to each primary object in the The category of the second time period uses heat; wherein the category identification model is trained to represent the category of the commodity object category in the second time period, and the commodity object category is in the second A recognition model of the relationship between the heat usage of the category of one or more historical time periods corresponding to the time period;
- the object heat update unit 608 may be configured to use, for each primary selection object, a heat in the category of the second time period according to the category corresponding to the primary selection object, and the second selected time in the second time period The objects of the segment are updated with heat.
- the commodity object selection device may further include a category identification sample data receiving unit 609 and a category identification model determining unit 610:
- the category identification sample data receiving unit 609 is configured to: at the one or more historical synchronization times corresponding to the second time period, based on the set category identification model and the category corresponding to each primary selection object
- the category of the segment uses the heat to determine that the category corresponding to each of the primary selected objects receives the category identification model training sample data sent by the user terminal before the category usage heat of the second time period, wherein the category identification
- the model training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category corresponding to the category identification model sample object at each fifth time
- the category usage heat of the segment, and the category corresponding to the category identification model sample object uses the heat in the category of one or more historical synchronization time periods corresponding to each fifth time period;
- the category identification model determining unit 610 is configured to use, according to the basic feature data of the sample objects of the various types of mesh recognition models, the heat usage and the commodity object used for predicting the category of the commodity object category in the second time period.
- the category is trained in an initial category recognition model of the association relationship between the categories of heat usage of one or more historical time periods corresponding to the second time period, and the category is obtained Identify the model.
- the historical time period of each time period refers to a historical time period that is on the same calendar day or lunar day as the time period and corresponds to the time period.
- the commodity object selection device may further include a category heat update unit 611:
- the category heat update unit 611 may be configured to use the heat in the category of the second time period according to the category corresponding to the primary selection object for any primary selection object, and the primary selection object is in the Before determining that the second time period is a specific time period, the object corresponding to the first time period is determined to be a specific time corresponding to the second time period. The category matched by the segment increases the category usage heat of the category corresponding to the primary selection object in the second time period according to the set coefficient.
- the commodity object selection device may further include a commodity object de-same unit 612:
- the commodity object de-same unit 612 can be configured to select at least one commodity object from the primary selection object as the commodity object in the second time period that matches the product object keyword, according to each primary selection object. Correlating degrees between corresponding titles, determining similarity between each primary selection object; for each set of object collections consisting of at least one primary selection object whose similarity between the two is not lower than the first similarity threshold , retain an object in the collection of objects, and delete other objects.
- the commodity object selection device may further include a keyword expansion unit 613:
- the keyword expansion unit 613 may be configured to determine, for each product object keyword sent by the user terminal, based on the set sample corpus, before acquiring the primary object that matches the product object keyword
- the similarity between the subject keywords is not lower than the at least one sample word of the second similarity threshold; the determined sample words are taken as the final desired commodity object keywords.
- the object recognition model may be a regression model; the category recognition model may be a linear model.
- the second embodiment of the present application further provides another product object selection device.
- the other product object selection device refer to the first embodiment of the method.
- the other commodity object selection device can mainly include:
- the keyword receiving unit 701 is configured to receive a product object keyword input by the user
- a keyword sending unit 702 configured to send the product object keyword to a server
- the object information receiving unit 703 is configured to receive the commodity object information returned by the server according to the product object keyword;
- the object determining unit 704 is configured to determine the product object corresponding to the product object information as the product object that matches the product object keyword in the second time period;
- the product object information is used by the server according to the set object recognition model and the primary selection objects that match the product object topic in the first time period, and the product data from the product.
- the set object recognition model is used to characterize the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period.
- the another commodity object selection device may further include an object recognition sample data receiving unit 705 and an object recognition sample data transmitting unit 706:
- the object recognition sample data receiving unit 705 is configured to receive the object recognition model training sample data input by the user before receiving the commodity object keyword input by the user, wherein the object recognition model training sample data includes each object recognition model Base feature data of the sample object, and the base feature data of each object recognition model sample object includes usage feature data of the object recognition model sample object in each third time period, and the object recognition model sample object is in each The object of the corresponding fourth time period uses the heat; the third time period is the previous specified time period of the corresponding fourth time period;
- the object identification sample data sending unit 706 is configured to send the object recognition model training sample data to a server, and the server identifies the basic features of each object recognition model sample object included in the sample data according to the object recognition model. Data, used in advance for predicting the use of feature data of the product object in the first time period, and the use of the object object in the second time period The initial object recognition model of the relationship between the two is trained to obtain the set object recognition model.
- the another commodity object selection device may further include a category identification sample data receiving unit 707 and a category identification sample data sending unit 708:
- the category identification sample data receiving unit 707 is configured to receive, after receiving the product object information returned by the server according to the product object subject word, the category identification model training sample data input by the user, wherein the category The recognition model training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category corresponding to the category identification model sample object in each fifth The category usage heat of the time period, and the category corresponding to the category identification model sample object uses the heat in the category of one or more historical synchronization time periods corresponding to each fifth time period;
- the category identification sample data sending unit 708 is configured to send the category identification model training sample data to a server, and the server trains the sample of various types of eye recognition models included in the sample data according to the category identification model.
- the basic feature data of the object, for the pre-established category use heat for predicting the item object category in the second time period, and one or more historical time periods corresponding to the item object category corresponding to the second time period.
- the category of the segment is trained using the initial category recognition model of the relationship between the heats, and the category used to characterize the commodity object category in the second time period is used, and the commodity object category is in the second time.
- the category identification model of the association relationship between the heats of the one or more historical time periods corresponding to the segments are examples of the association relationship between the heats of the one or more historical time periods corresponding to the segments.
- the second embodiment of the present application further provides a model determining device.
- the model determining device refer to the first model in the first embodiment of the method.
- the model determining device may mainly include:
- the data receiving unit 801 is configured to receive the object recognition model training sample data sent by the user terminal, where the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object recognition model sample
- the base feature data of the object includes the object Identifying the use feature data of the model sample object in each of the third time periods, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time period is the corresponding fourth time period The previous specified time period;
- the model training unit 802 is configured to: according to the basic feature data of each object recognition model sample object included in the object recognition model training sample data, the pre-established use feature data for predicting the commodity object in the first time period, Training with the initial object recognition model of the relationship between the object objects in the second time period and the heat usage of the objects, obtaining the use feature data for characterizing the product object in the first time period, and the second time period with the product object
- the object uses an object recognition model of the relationship between the heats; the first time period is a previous specified time period of the second time period.
- the second embodiment of the present application further provides another model determining device.
- the other model determining device refer to the related method in the first embodiment of the method. Another description of the method for determining the model is not repeated here.
- the other model determining device may mainly include:
- the data receiving unit 901 is configured to receive the category identification model training sample data sent by the user terminal, where the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each class
- the base feature data of the target model sample object includes the category corresponding to the category object of the sample identification model, and the fifth time period in the sample object selection method (in the foregoing, the fifth time period in the foregoing commodity object selection method;
- the commodity object selection method may also represent the set sample time period as the first time period to distinguish the second time period mentioned later by the model determining device, and the category use heat is not described herein.
- the category corresponding to the category identification model sample object uses heat in a category of one or more historical synchronization time periods corresponding to each set sample period;
- the model training unit 902 is configured to: according to the category identification data of the category identification model sample objects included in the sample data of the category identification model, the pre-established class for predicting the commodity object category in the second time period.
- the initial category identification of the relationship between the heat usage and the heat usage of the category of the commodity object category in one or more historical time periods corresponding to the second time period The model is trained to obtain a category used to characterize the category of the commodity object category in the second time period, and the category of the commodity object category in the one or more historical time periods corresponding to the second time period.
- the second embodiment of the present application further provides a heat determining device, and the specific implementation of the heat determining device can be referred to the heat usage in the first embodiment of the method.
- the heat determining device may mainly include:
- the data obtaining unit 1001 is configured to obtain usage characteristic data of each commodity object in a first time period
- the heat determining unit 1002 is configured to use an association relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and the product object in the first time period Using the feature data to determine the object usage heat of each commodity object in the second time period;
- the first time period is a previous specified time period of the second time period; and the association relationship is based on the use feature data of each sample object in each third time period and each corresponding sample object in each corresponding The object of the four time period is established using the heat; the third time period is the previous specified time period of the corresponding fourth time period.
- embodiments of the present application can be provided as a method, apparatus (device), or computer program product.
- the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
- the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
- the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
- the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
- These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
- the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
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Abstract
一种商品对象选取、模型确定及使用热度确定方法与装置,可基于用户(10)输入的商品对象主题词、以及设定的对象识别模型,从海量对象中自动选择出至少一个商品对象作为满足用户(10)需求的对象,从而大大提高了商品对象的选取效率,进而减少了人工盘货的成本,提高了运营效率。
Description
本申请涉及互联网技术领域,尤其涉及一种商品对象选取、模型确定及使用热度确定方法与装置。
为了提高电子商务***中的商品对象的成交性能,电子商务***常常会建立各种新的频道来增加商品对象曝光,比如,建立各种各样的特价秒杀活动主题频道、或者主打商品对象调性的主题频道等。
这些频道在刚建立之时都会碰到盘货问题,即,如何圈定适合该频道的商品对象,以达到频道成交最大化等。具体地,目前,为了解决上述问题,常采用人工方式为各新建频道选取相应的商品对象,即,由操作人员根据人为经验,主观去选择符合各新建频道所需主题的商品对象。
但是,由于采用人工方式进行商品对象的选取常常需要花费大量时间,从而使得商品对象的选取效率十分低下。
发明内容
本申请实施例提供了一种商品对象选取、模型确定及使用热度确定方法与装置,用以解决现有的商品对象选取方式所存在的效率低下的问题。
一方面,本申请实施例提供了一种商品对象选取方法,包括:
接收用户终端发送的商品对象主题词;
获取与所述商品对象主题词相匹配的初选对象;
基于设定的对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度;其中,所述对象识别模型为训练得到的用于表征商品对象在第一时间段内的使用特征数据、与商品对象
在第二时间段的对象使用热度之间的关联关系的识别模型;所述第一时间段为第二时间段的前一指定时间段;
根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
可选地,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象,包括:
从初选对象中选取至少一个对象使用热度不低于设定热度阈值的商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
可选地,在基于设定的对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度之前,所述方法还包括:
接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;
根据各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到所述对象识别模型。
可选地,在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,所述方法还包括:
基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度;其中,所述类目识别模型为训练得
到的用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的识别模型;
针对每一初选对象,根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新。
可选地,在基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度之前,所述方法还包括:
接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第五时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第五时间段相对应的一个或多个历史同期时间段的类目使用热度;
根据各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到所述类目识别模型。
其中,每一时间段的历史同期时间段是指与该时间段处于同一公历日或农历日下,且与该时间段相对应的历史时间段。
可选地,针对任一初选对象,在根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新之前,所述方法还包括:
若确定所述第二时间段为特定时间段,则若确定所述初选对象对应的类目为与所述第二时间段对应的特定时间段相匹配的类目,则根据设定的系数,增大所述初选对象对应的类目在所述第二时间段的类目使用热度。
可选地,在从初选对象中选取至少一个商品对象作为所述第二时间段内、
与所述商品对象主题词相匹配的商品对象之前,所述方法还包括:
根据各初选对象对应的标题之间的相似度,确定各初选对象之间的相似度;
针对每一组由相互之间的相似度不低于第一相似度阈值的至少一个初选对象所组成的对象集合,保留所述对象集合中的一对象,并删除其它对象。
可选地,在获取与所述商品对象主题词相匹配的初选对象之前,所述方法还包括:
针对用户终端发送的每一商品对象主题词,基于设定的样本语料,确定与该商品对象主题词之间的相似度不低于第二相似度阈值的至少一个样本词语;
将确定的各样本词语作为最终所需的商品对象主题词。
其中,所述对象识别模型为回归模型;所述类目识别模型为线性模型。
另一方面,本申请实施例提供了另一种商品对象选取方法,包括:
接收用户输入的商品对象主题词,并将所述商品对象主题词发送至服务器;
接收所述服务器根据所述商品对象主题词返回的商品对象信息,并将所述商品对象信息所对应的商品对象作为第二时间段内、与所述商品对象主题词相匹配的商品对象;
其中,所述商品对象信息是所述服务器根据设定的对象识别模型、以及与所述商品对象主题词相匹配的各初选对象在第一时间段内的使用特征数据,从与所述商品对象主题词相匹配的初选对象中所选取的商品对象的相关信息;所述第一时间段为第二时间段的前一指定时间段;
所述设定的对象识别模型用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系。
可选地,在接收用户输入的商品对象主题词之前,所述方法还包括:
接收用户输入的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识
别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;
将所述对象识别模型训练样本数据发送至服务器,由所述服务器根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到所述设定的对象识别模型。
又一方面,本申请实施例提供了一种模型确定方法,包括:
接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;
根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的对象识别模型;所述第一时间段为第二时间段的前一指定时间段。
其中,所述使用特征数据至少包括浏览次数、收藏次数、加购次数、成交次数、评论次数、以及搜索次数中的任意一种或多种;所述对象使用热度至少包括成交量、成交额、以及成交转化率中的任意一种或多种。
再一方面,本申请实施例提供了另一种模型确定方法,包括:
接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模
型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第一时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第一时间段相对应的一个或多个历史同期时间段的类目使用热度;
根据所述类目识别模型训练样本数据中包含的各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的类目识别模型。
其中,所述类目使用热度至少包括成交量、成交额、以及成交转化率中的任意一种或多种。
又一方面,本申请实施例还提供了一种使用热度确定方法,包括:
获取各商品对象在第一时间段内的使用特征数据;
基于商品对象在第一时间段内的使用特征数据与商品对象在第二时间段的对象使用热度之间的关联关系,以及,各商品对象在第一时间段内的使用特征数据,确定各商品对象在第二时间段的对象使用热度;
所述第一时间段为第二时间段的前一指定时间段;且,所述关联关系是根据各样本对象在各第三时间段的使用特征数据以及各样本对象在各对应的第四时间段的对象使用热度所建立的;所述第三时间段为对应的第四时间段的前一指定时间段。
又一方面,本申请实施例还提供了一种商品对象选取装置,包括:
主题词接收单元,用于接收用户终端发送的商品对象主题词;
对象获取单元,用于获取与所述商品对象主题词相匹配的初选对象;
热度确定单元,用于基于设定的对象识别模型以及各初选对象在第一时间
段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度;其中,所述对象识别模型为训练得到的用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的识别模型;所述第一时间段为第二时间段的前一指定时间段;
对象筛选单元,用于根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
另一方面,本申请实施例还提供了另一种商品对象选取装置,包括:
主题词接收单元,用于接收用户输入的商品对象主题词;
主题词发送单元,用于将所述商品对象主题词发送至服务器;
对象信息接收单元,用于接收所述服务器根据所述商品对象主题词返回的商品对象信息;
对象确定单元,用于将所述商品对象信息所对应的商品对象确定为第二时间段内、与所述商品对象主题词相匹配的商品对象;
其中,所述商品对象信息是所述服务器根据设定的对象识别模型、以及与所述商品对象主题词相匹配的各初选对象在第一时间段内的使用特征数据,从与所述商品对象主题词相匹配的初选对象中所选取的商品对象的相关信息;所述第一时间段为第二时间段的前一指定时间段;
所述设定的对象识别模型用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系。
又一方面,本申请实施例还提供了一种模型确定装置,包括:
数据接收单元,用于接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间
段的前一指定时间段;
模型训练单元,用于根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的对象识别模型。
另一方面,本申请实施例还提供了另一种模型确定装置,包括:
数据接收单元,用于接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第一时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第一时间段相对应的一个或多个历史同期时间段的类目使用热度;
模型训练单元,用于根据所述类目识别模型训练样本数据中包含的各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的类目识别模型。
再一方面,本申请实施例还提供了一种使用热度确定装置,包括:
数据获取单元,用于获取各商品对象在第一时间段内的使用特征数据;
热度确定单元,用于基于商品对象在第一时间段内的使用特征数据与商品对象在第二时间段的对象使用热度之间的关联关系,以及,各商品对象在第一时间段内的使用特征数据,确定各商品对象在第二时间段的对象使用热度;
所述第一时间段为第二时间段的前一指定时间段;且,所述关联关系是根据各样本对象在各第三时间段的使用特征数据以及各样本对象在各对应的第四时间段的对象使用热度所建立的;所述第三时间段为对应的第四时间段的前一指定时间段。
本申请有益效果如下:
本申请实施例提供了一种商品对象选取、模型确定及使用热度确定方法与装置,可基于用户输入的商品对象主题词以及设定的对象识别模型,从海量对象中自动选择出至少一个商品对象作为最终的满足用户需求的对象,从而大大提高了商品对象的选取效率,进而减少了人工盘货的成本,提高了运营效率。
为了更清楚地说明本申请实施例中的技术方案,下面将对实施例描述中所需要使用的附图作简要介绍,显而易见地,下面描述中的附图仅仅是本申请的一些实施例,对于本领域的普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1所示为本申请实施例一中的商品对象选取方法的一种可能的应用场景示意图;
图2所示为本申请实施例一中的商品对象选取方法的一种可能的流程示意图;
图3所示为本申请实施例一中的模型确定方法的一种可能的流程示意图;
图4所示为本申请实施例一中的另一种模型确定方法的一种可能的流程示意图;
图5所示为本申请实施例一中的一种使用热度确定方法的一种可能的流程示意图;
图6所示为本申请实施例二中的商品对象选取装置的一种可能的结构示意图;
图7所示为本申请实施例二中的另一种商品对象选取装置的一种可能的结构示意图;
图8所示为本申请实施例二中的模型确定装置的一种可能的结构示意图;
图9所示为本申请实施例二中的另一种模型确定装置的一种可能的结构示意图;
图10所示为本申请实施例二中的一种使用热度确定装置的一种可能的结构示意图。
为了使本申请的目的、技术方案和优点更加清楚,下面将结合附图对本申请作进一步地详细描述,显然,所描述的实施例仅仅是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例,都属于本申请保护的范围。
实施例一:
为了解决现有的商品对象选取方式所存在的效率低下的问题,本申请实施例一提供了一种商品对象选取方法,如图1所示,其为所述商品对象选取方法的一种可能的应用场景示意图,该场景例如可以包括:用户终端11以及服务器12,其中:
用户终端11可接收用户10输入的商品对象主题词,并将所述商品对象主题词发送至服务器12;服务器12可根据用户终端11发送的商品对象主题词,获取与所述商品对象主题词相匹配的初选对象,并基于设定的对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度,并根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象;用户终端11可接收所述服务器12在选取至少一个商品对象后返回的所述至少一个商品对象的商品对象信息,并将所述商品对象信息
所对应的商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象;其中,所述设定的对象识别模型可用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系;所述第一时间段为第二时间段的前一指定时间段。
其中,用户终端11和服务器12可通过通信网络进行通信连接,该网络可以为局域网、广域网等。用户终端11可以为手机、平板电脑、笔记本电脑、个人计算机等终端设备,甚至还可为安装于上述终端设备中的客户端;服务器12可以为任何能够支持商品对象的筛选等处理操作的服务器设备。
也就是说,本申请所述实施例中,可基于用户输入的商品对象主题词以及设定的对象识别模型,从海量对象中自动选择出至少一个商品对象作为最终的满足用户需求的对象,从而大大提高了商品对象的选取效率,进而减少了人工盘货的成本,提高了运营效率。
下面,将结合图1所示的应用场景,参考图2来对本申请实施例一中的商品对象选取方法进行示例性说明。需要注意的是,上述应用场景仅是为了便于理解本申请的精神和原理而示出,本申请的实施方式在此方面不受任何限制。相反,本申请的实施方式可以应用于适用的任何场景。
具体地,如图2所示,其为本申请实施例一中的商品对象选取方法的一种可能的流程示意图,所述商品对象选取方法可包括以下步骤:
步骤201:用户终端接收用户输入的对象识别模型训练样本数据,并将所述对象识别模型训练样本数据发送至服务器。
其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段。
可选地,各对象识别模型样本对象等商品对象的使用特征数据至少可包括
浏览次数、收藏次数、加购(加入购物车)次数、成交次数、评论次数、以及搜索次数中的任意一种或多种;各对象识别模型样本对象等商品对象的对象使用热度至少可包括成交量、成交额、成交转化率中的任意一种或多种。另外,需要说明的是,各对象识别模型样本对象等商品对象的使用特征数据可从各电子商务网站的商品对象运营信息中获取,各对象识别模型样本对象等商品对象的对象使用热度可基于各对象识别模型样本对象等商品对象的使用特征数据计算得到,此处不作赘述。
再有,需要说明的是,第三、第四时间段通常可为历史时间段,即,每一对象识别模型样本对象的基础特征数据通常可为相应的历史数据;且,第三、第四时间段、以及指定时间段等时间段的大小可根据实际情况灵活设置,如可设置为1天、1周、1月等等(通常最小为一天)。另外,第三、第四时间段的长度可相同也可不同,例如,第四时间段可为一天,而该第四时间段对应的第三时间段(即该第四时间段的前一指定时间段)可为一月或一年等,或者,第四时间段可为一月,而该第四时间段对应的第三时间段可为一天等;再有,第四时间段对应的第三时间段通常可为与该第四时间段相邻的前一指定时间段,当然,也可不相邻,对此不作限定。
步骤202:服务器接收用户终端发送的对象识别模型训练样本数据,并根据对象识别模型训练样本数据中的各对象识别模型样本对象的基础特征数据,对预先建立的初始对象识别模型进行训练,得到所需的对象识别模型。
其中,所述初始对象识别模型为用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的识别模型;所述对象识别模型为训练得到的用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的识别模型,所述第一时间段为第二时间段的前一指定时间段。
另外,与前述关于第三、第四时间段的描述相类似,第一、第二时间段等时间段的大小也可根据实际情况灵活设置,且,第一、第二时间段的长度可相
同也可不同(不过,第一时间段的大小通常可与第三时间段相同,第二时间段的大小通常可与第四时间段相同),对此不作限定。
可选地,以各对象识别模型样本对象等商品对象的对象使用热度为成交量为例,可通过以下步骤训练得到相应的对象识别模型:
A1:建立与成交量相关的初始对象识别模型。
可选地,假设第三、第四时间段均可设置为1天;且假设可以y(t)表示某一商品对象在日期t的成交量,以x1(t-1)表示该商品对象在日期t-1的浏览次数,以x2(t-1)表示该商品对象在日期t-1的收藏次数,以x3(t-1)表示该商品对象在日期t-1的加购次数等等,以及,假设初始对象识别模型为线性模型;则建立的初始对象识别模型可表示为y(t)=a(1)*x1(t-1)+a(2)*x2(t-1)+a(3)*x3(t-1)+…,其中,a(1)、a(2)、a(3)、…等为需要估计的系数。
A2:计算得到各对象识别模型样本对象在每一第四时间段的成交量(如在日期t的日销量),并与各对象识别模型样本对象在对应的第三时间段的使用特征数据(如日期t-1的浏览、收藏、加购、成交、评论、搜索等特征数据)进行关联,得到多个关联数据;并基于得到的各关联数据,对建立的初始对象识别模型进行训练,得到a(1)、a(2)、a(3)等各系数的实际取值,以得到所需的对象识别模型。
需要说明的是,由于相对于线性模型来说,回归模型能够很好地交叉特征,提高预测能力并能够防止商家作弊,进而提高预测的准确性,因而,本实施例中,优选地,所述初始对象识别模型以及所述对象识别模型通常可为回归模型,如Gradient Boost Regression Tree(渐进梯度回归树)模型等。当然,如果对预测的准确性要求相对较低,所述初始对象识别模型以及所述对象识别模型也可采用线性模型,此处不作限定。
另外,需要说明的是,在训练得到所述对象识别模型之后,还可根据最新的对象识别模型样本数据实时或定时对所述对象识别模型进行更新,以提高所述对象识别模型的准确性。再有,以为某一频道选取商品对象为例,待该频道
上线后,还可将各对象识别模型样本对象在各第三时间段内的使用特征数据、以及各对象识别模型样本对象在各对应的第四时间段的对象使用热度等分别替换为各对象识别模型样本对象在该频道下的相应的使用特征数据以及对象使用热度等,以更好地对商品对象在该频道下的对象使用热度进行预测,此处不再赘述。
再有,需要说明的是,本实施例中,步骤201以及步骤202为预先建立对象识别模型的步骤,并不是每次进行商品对象的选取时均需要执行的步骤,除非对象识别模型训练样本数据发生了相应更新。即,在执行完步骤201以及步骤202之后,可以多次重复执行后续各步骤,对此不作赘述。
步骤203:用户终端接收用户输入的商品对象主题词,并将所述商品对象主题词发送至服务器。
可选地,用户终端在接收到用户输入的商品对象主题词之后,除了可直接将接收到的商品对象主题词发送至服务器之外,还可对接收到的商品对象主题词进行扩充,并将扩充后的商品对象主题词发送至服务器,以提高商品对象主题词的丰富性。另外,由用户终端对用户输入的商品对象主题词进行扩充,还可避免当有大量用户终端同时向服务器发送商品对象主题词时,服务器需同时对接收到的大量商品对象主题词进行扩充的情况发生,以节省服务器的处理资源、减轻服务器的工作压力,进而可进一步提高后续商品对象选取的速度以及效率。
可选地,用户终端可通过以下方式对接收到的商品对象主题词进行扩充:
针对接收到的每一商品对象主题词,基于设定的样本语料,确定与该商品对象主题词之间的相似度不低于设定的相似度阈值(该阈值可根据实际情况灵活设置)的至少一个样本词语;将确定的各样本词语作为最终所需的商品对象主题词。
其中,所述设定的样本语料可为通过爬虫从外部网站上爬取到的电商新闻等语料库;另外,在基于设定的样本语料,确定与各商品对象主题词之间的相
似度不低于设定的相似度阈值的至少一个样本词语时,可首先基于所述设定的样本语料训练word2vec模型等能够将词表征为实数值向量的语言模型,并基于训练后的word2vec模型等语言模型,将用户输入的各商品对象主题词以及样本语料中的各词语转化为向量;之后,可利用设定的相似度计算公式,如Cosine公式等,计算样本语料中的各词语与用户输入的各商品对象主题词之间的相似度;最后,通过设置相应的相似度阈值选择该值以上(可包含该值)的词语作为最终所需的主题词语。
例如,假设用户根据实际需求向用户终端输入了以下三个商品对象主题词:“快时尚”、“男装”,“女装”,则用户终端可基于设定的样本语料,对该三个词语进行扩充,如扩充得到“时尚”、“潮流”、“衬衫”、“西装”、“连衣裙”、“牛仔裤”等词语,并将扩充后的各词语作为最终的商品对象主题词。
步骤204:服务器接收用户终端发送的商品对象主题词,并获取与所述商品对象主题词相匹配的初选对象。
可选地,服务器可根据用户终端发送的商品对象主题词,基于文本挖掘的方法,从各电子商务网站的商品对象信息中,搜索对应的商品对象标题与用户终端发送的商品对象主题词相匹配(如部分匹配等)的商品对象,并将搜索到的各商品对象作为与用户终端发送的商品对象主题词相匹配的初选对象。
其中,电子商务网站中的每一商品对象信息可包括商品对象的ID(标识)、名称(即标题)、产地、卖家用户信息、类目等基本信息,此处不作赘述。
另外,可选地,服务器在根据接收到的用户终端发送的商品对象主题词,获取与所述商品对象主题词相匹配的初选对象之前,还可对接收到的商品对象主题词进行扩充,以便基于扩充后的商品对象主题词获取相应的初选对象。
其中,服务器对接收到的商品对象主题词进行扩充的具体实施方式与步骤203中用户终端对接收到的商品对象主题词进行扩充的具体实施方式相类似,对此不作赘述。
需要说明的是,由服务器而非用户终端来执行对用户输入的商品对象主题
词进行扩充的操作,可降低对用户终端的性能要求,使得本申请实施例所述的方法适用范围更广;另外,对于用户终端而言,也可节省用户终端的处理资源、减轻用户终端的工作压力。
步骤205:服务器基于训练得到的所述对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度。
例如,假设所述对象识别模型为服务器根据各对象识别模型样本对象在各日期的成交量,以及各对象识别模型样本对象在对应的日期之前的一个或多个日期的浏览、收藏、加购、成交、评论、搜索等特征数据所训练得到的对象识别模型,则可基于该对象识别模型,根据各初选对象在日期t+1之前的一个或多个日期的浏览、收藏、加购、成交、评论、搜索等特征数据,预测各初选对象在日期t+1的成交量。
需要说明的是,特殊地,本实施例中,当各商品对象的对象使用热度为成交额时,由于成交额的范围较大,可能无法很好预测,因而,还可不直接预测成交额,而是先基于与成交量相关的对象识别模型预测各商品对象的成交量,之后,再乘以对应的价格得到成交额,以提高预测的准确性。
步骤206:服务器根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
可选地,为了提高所选取的商品对象的准确性,服务器可根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个对象使用热度不低于设定热度阈值(该阈值可根据实际情况灵活设置)的商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
另外,服务器还可以按照对象使用热度从大到小的顺序,对各初选对象进行排序,并取前K(K为任意正整数)个初选对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
进一步地,为了提高所选取的商品对象的准确性,在从初选对象中选取至
少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,还可根据实际需求,对各初选对象进行人工筛选,或删除短期对象使用热度不高或价格不符合用户需求的商品对象(比如三天之内仅成交5件,价格在10到200元之间等的商品对象),以便基于筛选后的各初选对象选取所需的商品对象;和/或,
在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之后,还可以根据实际需求,对选取的商品对象进行人工筛选,或删除短期对象使用热度不高或价格不符合用户需求的商品对象,并将筛选后的各商品对象作为最终所需的所述第二时间段内、与所述商品对象主题词相匹配的商品对象,对此均不作赘述。
进一步地,由于对于部分商品对象来说,其具有明显的季节性,比如6月穿连衣裙,9月穿大衣等,且,买家通常会提前购买该类商品对象,但该类商品对象在前述提前购买时间段的对象使用热度并不会很大,导致采用之前的预测方式无法使这些商品对象排在靠前的位置。因而,为了解决这种问题,在确定各初选对象在所述第二时间段的对象使用热度之后,还可根据时间信息调整各初选对象的对象使用热度、进而调整各初选对象的排序,以便根据热度调整后的各初选对象,选取最终所需的商品对象。即可利用时间序列模型,对类目在第二时间段的热度进行预测,从而使一些应季商品对象能够提前浮现,以进一步提高商品对象选取的准确性。
也就是说,在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,所述方法还可包括:
基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度;其中,所述类目识别模型为训练得到的用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的
关联关系的识别模型;
针对每一初选对象,根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新。
可选地,针对每一初选对象,可将所述初选对象对应的类目在所述第二时间段的类目使用热度与所述初选对象在所述第二时间段的类目使用热度的乘积,或者二者的加权和(二者对应的权重可根据实际情况灵活设定),作为所述初选对象在所述第二时间段的更新后的对象使用热度。
其中,与对象使用热度相类似,所述类目使用热度至少可包括成交量、成交额、以及成交转化率中的任意一种或多种,且,各样本对象等商品对象的类目使用热度可基于各样本对象等商品对象的使用特征数据计算得到,对此不作限定。另外,每一时间段的历史同期时间段是指与该时间段处于同一公历日或农历日下,且与该时间段相对应的历史时间段;例如,针对时间段2016年01月01日~2016年01月05日而言,该时间段的历史同期时间段可为2015年01月01日~2015年01月05日、2014年01月01日~2014年01月05日等等,对此也不作赘述。
可选地,本实施例中,在基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度之前,服务器可通过以下方式得到所述类目识别模型:
接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第五时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第五时间段相对应的一个或多个历史同期时间段的类目使用热度;
根据各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段
相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到所述类目识别模型。
其中,需要说明的是,第五时间段通常可为历史时间段;且,与前述关于第一、第二、第三、第四时间段的描述相类似,第五时间段的大小也可根据实际情况灵活设置(不过,第五时间段的大小通常可与第二时间段相同),对此不作限定。
可选地,以各类目识别模型样本对象的类目使用热度为成交量为例,可通过以下步骤训练得到相应的类目识别模型:
B1:建立与成交量相关的初始类目识别模型。
例如,假设各第五时间段可设置为1个月;初始类目识别模型为线性模型;且假设今年t月某一类目的成交量为z(t),去年同期该类目的成交量为z(t-1),前年同期该类目的成交量为z(t-2),依此类推;则建立的初始类目识别模型可表示为z(t)=b(1)*z(t-1)+b(2)*z(t-2)+…,其中,b(1)、b(2)、…等为需要估计的系数。
需要说明的是,之所以使用线性模型作为类目识别模型,是因为模型参数少且类目的历史数据会比较稳定。当然,也可用其它模型,如回归模型作为类目识别模型,以提高类目使用热度预测的准确性,此处不作限定。
B2:利用最新一段时间的类目历史数据(比如今年最新的3个月的类目历史成交量等)以及其所对应的同期历史数据(如同一个公历日或农历日下的至少两年的同期历史数据),对建立的初始类目识别模型进行训练,得到b(1)、b(2)等各系数的实际取值,以得到最终所需的类目识别模型。
另外,需要说明的是,在训练得到所述类目识别模型之后,还可根据最新的类目识别模型样本数据实时或定时对所述类目识别模型进行更新,以提高所述类目识别模型的准确性;再有,以为某一频道选取商品对象为例,待该频道上线后,还可将各类目识别模型样本对象在各第五时间段的类目使用热度、以及各类目识别模型样本对象在与各第五时间段相对应的一个或多个历史同期时间段的类目使用热度等分别替换为各类目识别模型样本对象在该频道下的
相应的类目使用热度等,以更好地对商品对象类目在该频道下的类目使用热度进行预测,此处不再赘述。
进一步地,在按照上述方式得到所述类目识别模型之后,若确定所述第二时间段为下一个月,则可基于训练得到的所述类目识别模型、以及各初选对象对应的类目在与所述下一个月相对应的一个或多个历史同期时间段(如,前一年或前两年等历史同期时间段)的类目使用热度,确定各初选对象对应的类目在所述下一个月的类目使用热度。
另外,需要说明的是,除了可按照上述方式预测下一个月的相关商品对象类目的类目使用热度之外,还可以天为单位,建立相应的初始类目识别模型,如建立如下初始类目识别模型z1(t)=b1(1)*z1(t-1)+b1(2)*z1(t-2)+…,其中,z1(t)为今年日期t某一类目的类目使用热度,z1(t-1)为去年同期该类目的类目使用热度,z1(t-2)为前年同期该类目的类目使用热度,依此类推;b1(1)、b1(2)等为需要估计的系数;之后,可利用最新一段时间的类目历史数据(比如90天等)以及其所对应的同期历史数据,对建立的初始类目识别模型进行训练,得到b1(1)、b1(2)等各系数的取值,以得到最终所需的类目识别模型;再之后,利用训练得到的类目识别模型,向后计算30天的各类目的类目使用热度,然后,加和平均得到各类目后一个月较为稳定的类目使用热度,此处不再赘述。
进一步地,为了使得所选取的商品对象更为符合用户需求,以提高商品对象的成交性能,当确定所述第二时间段为节假日等特定时间段时,还可对与该第二时间段对应的特定时间段相关的各类目的类目使用热度进行额外加权(额外加权的程度可根据实际需求而定),确保这些类目对应的商品对象能够及时浮现。例如,中秋节月饼会大热,因而,当所述第二时间段为中秋节时间段时,还可对月饼对应的类目进行额外加权。
也就是说,针对任一初选对象,在根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新之前,所述方法还可包括:
若确定所述第二时间段为特定时间段(如中秋节、端午节等节假日时间段等),则若确定所述初选对象对应的类目为与所述第二时间段对应的特定时间段相匹配的类目,则根据设定的系数(该系数可根据实际情况灵活调整,如,若类目与特定时间段的匹配程度较高,则系数可较大,若匹配程度较低,则系数可较小等),增大所述初选对象对应的类目在所述第二时间段的类目使用热度。
进一步地,由于直接将各商品对象按对象使用热度进行排序会导致有一些同质商品对象的出现,比如“中秋节礼物意大利进口费列罗巧克力玫瑰花DIY礼盒装生日情人包邮”和“顺丰包邮意大利费列罗巧克力DIY心形玫瑰礼盒装中秋节生日礼物”这两种商品对象非常类似,如果直接一并展示到前台会导致商品对象单一。因而,在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,还可对各初选对象进行去同操作。
即,在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,所述方法还可包括:
根据各初选对象对应的标题之间的相似度,确定各初选对象之间的相似度;
针对每一组由相互之间的相似度不低于设定的相似度阈值(该相似度阈值与前文中第一次提及的相似度阈值可相同或不同)的至少一个初选对象所组成的对象集合,保留所述对象集合中的一对象,并删除其它对象,以便后续可从执行删除操作后所得到的各初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
即,可通过计算商品对象标题的相似度得到各商品对象之间的相似度。另外,相似度计算可以采用杰卡德相似度公式J(A,B)=|A交B|/|A并B|(即,两个标题的相似度为两个标题共同词语的数量除以两个标题所有词语的数量)。例如,假设标题A是“榛子巧克力”,标题B是“牛奶巧克力”,则两者的
相似度为1/3,因为标题交集有一个词语“巧克力”,而两个标题的并集有三个词语。当然,也可采用其它任意的相似度计算公式计算得到各商品对象之间的相似度,对此不作限定。
再有,针对每一组由相互之间的相似度不低于设定的相似度阈值的至少一个初选对象所组成的对象集合,在保留所述对象集合中的一对象时,通常可保留对应的对象使用热度最高的一对象,以提高商品对象的成交性能,提高用户的应用体验。
此外,以为某一频道选取商品对象为例,若该频道需要实现商品对象的每日更新,则还可以通过配置相关逻辑,使得该频道最终展示的商品对象几天之内不重复,此处不再赘述。
进一步地,本实施例中,除了可在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,对各初选对象进行去同操作之外,还可在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之后,采用类似方式对所选取的各商品对象进行去同操作。
另外,服务器在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象后,还可将所选取的商品对象的商品对象信息(如商品对象的ID、或商品对象的链接等唯一性标识信息)进行存储,和/或,发送给用户终端。
步骤207:用户终端接收所述服务器返回的与所述商品对象主题词相匹配的商品对象的商品对象信息,并将所述商品对象信息所对应的商品对象作为第二时间段内、与所述商品对象主题词相匹配的商品对象。
可选地,用户终端还可将接收到的商品对象信息进行显示,和/或,将所述商品对象信息所对应的商品对象进行显示,以便用户查看,对此不作赘述。
另外,需要说明的是,上述步骤201、步骤203以及步骤207独立地构成了在用户终端侧执行的商品对象选取流程,步骤202、步骤204~步骤206独立
地构成了在服务器侧执行的商品对象选取流程,对此不作赘述。
进一步地,如图3、图4所示,本申请实施例还提供了两种模型确定方法。具体地,如图3所示,其为本申请实施例一中的一种模型确定方法的一种可能的流程示意图,所述模型确定方法可包括以下步骤:
步骤301:接收用户终端发送的对象识别模型训练样本数据。
其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段。
步骤302:根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的初始对象识别模型进行训练,得到所需的对象识别模型。
其中,所述初始对象识别模型用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系;所述第一时间段为第二时间段的前一指定时间段。
所述对象识别模型用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系。
进一步地,如图4所示,其为本申请实施例一中的另一种模型确定方法的一种可能的流程示意图,所述另一种模型确定方法可包括以下步骤:
步骤401:接收用户终端发送的类目识别模型训练样本数据。
其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各设定的样本时间段(如前述商品对象选取方法中的第五时间段;另外,若不考虑前述商品对象选取方法,也可将该设定的样本时间段表示为第一时间段,以与本模型确定方法后续提及的第二时间段进
行区分,对此不作赘述)的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各设定的样本时间段相对应的一个或多个历史同期时间段的类目使用热度。
步骤402:根据所述类目识别模型训练样本数据中包含的各类目识别模型样本对象的基础特征数据,对预先建立的初始类目识别模型进行训练,得到所需的类目识别模型。
其中,所述初始类目识别模型用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系。
所述类目识别模型用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系。
另外,需要说明的是,图3、图4所示的模型确定方法的执行主体均可为服务器;且,图3、图4所示的模型确定方法的各步骤的具体实施可参见前述相关描述,对此均不作赘述。
进一步地,如图5所示,本申请实施例还提供了一种使用热度确定方法。所述使用热度确定方法可包括以下步骤:
步骤501:获取各商品对象在第一时间段内的使用特征数据;
步骤502:基于商品对象在第一时间段内的使用特征数据与商品对象在第二时间段的对象使用热度之间的关联关系,以及,各商品对象在第一时间段内的使用特征数据,确定各商品对象在第二时间段的对象使用热度;
其中,所述第一时间段为第二时间段的前一指定时间段;且,所述关联关系是根据各样本对象在各第三时间段的使用特征数据以及各样本对象在各对应的第四时间段的对象使用热度所建立的;所述第三时间段为对应的第四时间段的前一指定时间段。
需要说明的是,所述关联关系即类似于前述所述的设定的对象识别模型;另外,图5所示的使用热度确定方法的各步骤的具体实施可参见前述相关描述,对此不作赘述。
由本申请实施例一所述内容可知,在本申请实施例一所述方案中,可基于用户输入的商品对象主题词以及设定的对象识别模型,从海量对象中自动选择出至少一个商品对象作为最终的满足用户需求的对象,从而大大提高了商品对象的选取效率,进而减少了人工盘货的成本,提高了运营效率。
另外,由于可根据各初选对象在第二时间段的对象使用热度,从初选对象中选取至少一个对象使用热度不低于设定热度阈值的商品对象作为最终所需的商品对象,从而还可提高商品对象选取的准确性。
此外,由于还可基于时间序列模型,即类目识别模型,计算类目的类目使用热度,并根据类目的类目使用热度,对各商品对象的排序进行调整,从而还可根据季节以及节假日等及时调整商品对象,以进一步减少人工盘货的成本,提高运营效率。
最后,需要说明的是,本申请实施例所述方案无语言、软件或者硬件的限制,基于一般的云计算平台即可实现。但是,为了提高频道对象的选取效率,可优先选用性能高的编程语言(如C、C++或者Java等)和性能高的硬件等来实现,本申请实施例对此不作赘述。
实施例二:
基于与本申请实施例一相同的发明构思,本申请实施例二提供了一种商品对象选取装置,该商品对象选取装置的具体实施可参见上述方法实施例一中的有关服务器的相关描述,重复之处不再赘述,如图6所示,该商品对象选取装置主要可包括:
主题词接收单元601,可用于接收用户终端发送的商品对象主题词;
对象获取单元602,可用于获取与所述商品对象主题词相匹配的初选对象;
热度确定单元603,可用于基于设定的对象识别模型以及各初选对象在第
一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度;其中,所述对象识别模型为训练得到的用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的识别模型;所述第一时间段为第二时间段的前一指定时间段;
对象筛选单元604,可用于根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
可选地,所述对象筛选单元604,具体可用于从初选对象中选取至少一个对象使用热度不低于设定热度阈值的商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
可选地,所述商品对象选取装置还可包括对象识别样本数据接收单元605以及对象识别模型确定单元606:
所述对象识别样本数据接收单元605,可用于在基于设定的对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在所述第二时间段的对象使用热度之前,接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;
所述对象识别模型确定单元606,可用于根据各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到所述对象识别模型。
可选地,所述商品对象选取装置还可包括类目热度确定单元607以及对象热度更新单元608:
所述类目热度确定单元607,可用于在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度;其中,所述类目识别模型为训练得到的用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的识别模型;
对象热度更新单元608,可用于针对每一初选对象,根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新。
可选地,所述商品对象选取装置还可包括类目识别样本数据接收单元609以及类目识别模型确定单元610:
所述类目识别样本数据接收单元609,可用于在基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度之前,接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第五时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第五时间段相对应的一个或多个历史同期时间段的类目使用热度;
所述类目识别模型确定单元610,可用于根据各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到所述类目
识别模型。
其中,每一时间段的历史同期时间段是指与该时间段处于同一公历日或农历日下,且与该时间段相对应的历史时间段。
可选地,所述商品对象选取装置还可包括类目热度更新单元611:
所述类目热度更新单元611,可用于针对任一初选对象,在根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新之前,若确定所述第二时间段为特定时间段,则若确定所述初选对象对应的类目为与所述第二时间段对应的特定时间段相匹配的类目,则根据设定的系数,增大所述初选对象对应的类目在所述第二时间段的类目使用热度。
可选地,所述商品对象选取装置还可包括商品对象去同单元612:
所述商品对象去同单元612,可用于在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,根据各初选对象对应的标题之间的相似度,确定各初选对象之间的相似度;针对每一组由相互之间的相似度不低于第一相似度阈值的至少一个初选对象所组成的对象集合,保留所述对象集合中的一对象,并删除其它对象。
可选地,所述商品对象选取装置还可包括主题词扩充单元613:
所述主题词扩充单元613,可用于在获取与所述商品对象主题词相匹配的初选对象之前,针对用户终端发送的每一商品对象主题词,基于设定的样本语料,确定与该商品对象主题词之间的相似度不低于第二相似度阈值的至少一个样本词语;将确定的各样本词语作为最终所需的商品对象主题词。
另外,需要说明的是,所述对象识别模型可为回归模型;所述类目识别模型可为线性模型。
进一步地,基于与本申请实施例一相同的发明构思,本申请实施例二还提供了另一种商品对象选取装置,该另一种商品对象选取装置的具体实施可参见上述方法实施例一中的有关用户终端的相关描述,重复之处不再赘述,如图7
所示,该另一种商品对象选取装置主要可包括:
主题词接收单元701,可用于接收用户输入的商品对象主题词;
主题词发送单元702,可用于将所述商品对象主题词发送至服务器;
对象信息接收单元703,可用于接收所述服务器根据所述商品对象主题词返回的商品对象信息;
对象确定单元704,可用于将所述商品对象信息所对应的商品对象确定为第二时间段内、与所述商品对象主题词相匹配的商品对象;
其中,所述商品对象信息是所述服务器根据设定的对象识别模型、以及与所述商品对象主题词相匹配的各初选对象在第一时间段内的使用特征数据,从与所述商品对象主题词相匹配的初选对象中所选取的商品对象的相关信息;所述第一时间段为第二时间段的前一指定时间段;
所述设定的对象识别模型用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系。
可选地,所述另一种商品对象选取装置还可包括对象识别样本数据接收单元705以及对象识别样本数据发送单元706:
所述对象识别样本数据接收单元705,可用于在接收用户输入的商品对象主题词之前,接收用户输入的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;
所述对象识别样本数据发送单元706,可用于将所述对象识别模型训练样本数据发送至服务器,由所述服务器根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度
之间的关联关系的初始对象识别模型进行训练,得到所述设定的对象识别模型。
可选地,所述另一种商品对象选取装置还可包括类目识别样本数据接收单元707以及类目识别样本数据发送单元708:
所述类目识别样本数据接收单元707,可用于在接收所述服务器根据所述商品对象主题词返回的商品对象信息之前,接收用户输入的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第五时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第五时间段相对应的一个或多个历史同期时间段的类目使用热度;
所述类目识别样本数据发送单元708,可用于将所述类目识别模型训练样本数据发送至服务器,由所述服务器根据所述类目识别模型训练样本数据中包含的各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的类目识别模型。
进一步地,基于与本申请实施例一相同的发明构思,本申请实施例二还提供了一种模型确定装置,该模型确定装置的具体实施可参见上述方法实施例一中的有关第一种模型确定方法的相关描述,重复之处不再赘述,如图8所示,该模型确定装置主要可包括:
数据接收单元801,可用于接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象
识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;
模型训练单元802,可用于根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的对象识别模型;所述第一时间段为第二时间段的前一指定时间段。
进一步地,基于与本申请实施例一相同的发明构思,本申请实施例二还提供了另一种模型确定装置,该另一种模型确定装置的具体实施可参见上述方法实施例一中的有关另一种模型确定方法的相关描述,重复之处不再赘述,如图9所示,该另一种模型确定装置主要可包括:
数据接收单元901,可用于接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各设定的样本时间段(如前述商品对象选取方法中的第五时间段;另外,若不考虑前述商品对象选取方法,也可将该设定的样本时间段表示为第一时间段,以与本模型确定装置后续提及的第二时间段进行区分,对此不作赘述)的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各设定的样本时间段相对应的一个或多个历史同期时间段的类目使用热度;
模型训练单元902,可用于根据所述类目识别模型训练样本数据中包含的各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别
模型进行训练,得到用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的类目识别模型。
进一步地,基于与本申请实施例一相同的发明构思,本申请实施例二还提供了一种使用热度确定装置,该使用热度确定装置的具体实施可参见上述方法实施例一中的有关使用热度确定方法的相关描述,重复之处不再赘述,如图10所示,该使用热度确定装置主要可包括:
数据获取单元1001,可用于获取各商品对象在第一时间段内的使用特征数据;
热度确定单元1002,可用于基于商品对象在第一时间段内的使用特征数据与商品对象在第二时间段的对象使用热度之间的关联关系,以及,各商品对象在第一时间段内的使用特征数据,确定各商品对象在第二时间段的对象使用热度;
其中,所述第一时间段为第二时间段的前一指定时间段;且,所述关联关系是根据各样本对象在各第三时间段的使用特征数据以及各样本对象在各对应的第四时间段的对象使用热度所建立的;所述第三时间段为对应的第四时间段的前一指定时间段。
最后,需要说明的是,本申请实施例说明书以及附图中的任何命名(如第一时间段、第二时间段等)都仅用于区分,而不具有任何限制含义。
本领域技术人员应明白,本申请的实施例可提供为方法、装置(设备)、或计算机程序产品。因此,本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且,本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、装置(设备)和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或
方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器,使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中,使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品,该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上,使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理,从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
尽管已描述了本申请的优选实施例,但本领域内的技术人员一旦得知了基本创造性概念,则可对这些实施例作出另外的变更和修改。所以,所附权利要求意欲解释为包括优选实施例以及落入本申请范围的所有变更和修改。
显然,本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样,倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内,则本申请也意图包含这些改动和变型在内。
Claims (22)
- 一种商品对象选取方法,其特征在于,包括:接收用户终端发送的商品对象主题词;获取与所述商品对象主题词相匹配的初选对象;基于设定的对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度;其中,所述对象识别模型为训练得到的用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的识别模型;所述第一时间段为第二时间段的前一指定时间段;根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
- 如权利要求1所述的方法,其特征在于,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象,包括:从初选对象中选取至少一个对象使用热度不低于设定热度阈值的商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
- 如权利要求1所述的方法,其特征在于,在基于设定的对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度之前,所述方法还包括:接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;根据各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到所述对象识别模型。
- 如权利要求1所述的方法,其特征在于,在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,所述方法还包括:基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度;其中,所述类目识别模型为训练得到的用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的识别模型;针对每一初选对象,根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新。
- 如权利要求4所述的方法,其特征在于,在基于设定的类目识别模型、以及各初选对象对应的类目在与所述第二时间段相对应的一个或多个历史同期时间段的类目使用热度,确定各初选对象对应的类目在所述第二时间段的类目使用热度之前,所述方法还包括:接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第五时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第五时间段相对应的一个或多个历史同期时间段的类目使用热度;根据各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始 类目识别模型进行训练,得到所述类目识别模型。
- 如权利要求4或5所述的方法,其特征在于,每一时间段的历史同期时间段是指与该时间段处于同一公历日或农历日下,且与该时间段相对应的历史时间段。
- 如权利要求4所述的方法,其特征在于,针对任一初选对象,在根据所述初选对象对应的类目在所述第二时间段的类目使用热度,对所述初选对象在所述第二时间段的对象使用热度进行更新之前,所述方法还包括:若确定所述第二时间段为特定时间段,则若确定所述初选对象对应的类目为与所述第二时间段对应的特定时间段相匹配的类目,则根据设定的系数,增大所述初选对象对应的类目在所述第二时间段的类目使用热度。
- 如权利要求1所述的方法,其特征在于,在从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象之前,所述方法还包括:根据各初选对象对应的标题之间的相似度,确定各初选对象之间的相似度;针对每一组由相互之间的相似度不低于第一相似度阈值的至少一个初选对象所组成的对象集合,保留所述对象集合中的一对象,并删除其它对象。
- 如权利要求1所述的方法,其特征在于,在获取与所述商品对象主题词相匹配的初选对象之前,所述方法还包括:针对用户终端发送的每一商品对象主题词,基于设定的样本语料,确定与该商品对象主题词之间的相似度不低于第二相似度阈值的至少一个样本词语;将确定的各样本词语作为最终所需的商品对象主题词。
- 如权利要求4所述的方法,其特征在于,所述对象识别模型为回归模型;所述类目识别模型为线性模型。
- 一种商品对象选取方法,其特征在于,包括:接收用户输入的商品对象主题词,并将所述商品对象主题词发送至服务 器;接收所述服务器根据所述商品对象主题词返回的商品对象信息,并将所述商品对象信息所对应的商品对象作为第二时间段内、与所述商品对象主题词相匹配的商品对象;其中,所述商品对象信息是所述服务器根据设定的对象识别模型、以及与所述商品对象主题词相匹配的各初选对象在第一时间段内的使用特征数据,从与所述商品对象主题词相匹配的初选对象中所选取的商品对象的相关信息;所述第一时间段为第二时间段的前一指定时间段;所述设定的对象识别模型用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系。
- 如权利要求11所述的方法,其特征在于,在接收用户输入的商品对象主题词之前,所述方法还包括:接收用户输入的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;将所述对象识别模型训练样本数据发送至服务器,由所述服务器根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到所述设定的对象识别模型。
- 一种模型确定方法,其特征在于,包括:接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第 三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的对象识别模型;所述第一时间段为第二时间段的前一指定时间段。
- 如权利要求13所述的方法,其特征在于,所述使用特征数据至少包括浏览次数、收藏次数、加购次数、成交次数、评论次数、以及搜索次数中的任意一种或多种;所述对象使用热度至少包括成交量、成交额、以及成交转化率中的任意一种或多种。
- 一种模型确定方法,其特征在于,包括:接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模型样本对象对应的类目在各第一时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第一时间段相对应的一个或多个历史同期时间段的类目使用热度;根据所述类目识别模型训练样本数据中包含的各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的类目识别模型。
- 如权利要求15所述的方法,其特征在于,所述类目使用热度至少包括成交量、成交额、以及成交转化率中的任意一种或多种。
- 一种使用热度确定方法,其特征在于,包括:获取各商品对象在第一时间段内的使用特征数据;基于商品对象在第一时间段内的使用特征数据与商品对象在第二时间段的对象使用热度之间的关联关系,以及,各商品对象在第一时间段内的使用特征数据,确定各商品对象在第二时间段的对象使用热度;所述第一时间段为第二时间段的前一指定时间段;且,所述关联关系是根据各样本对象在各第三时间段的使用特征数据以及各样本对象在各对应的第四时间段的对象使用热度所建立的;所述第三时间段为对应的第四时间段的前一指定时间段。
- 一种商品对象选取装置,其特征在于,包括:主题词接收单元,用于接收用户终端发送的商品对象主题词;对象获取单元,用于获取与所述商品对象主题词相匹配的初选对象;热度确定单元,用于基于设定的对象识别模型以及各初选对象在第一时间段内的使用特征数据,确定各初选对象在第二时间段的对象使用热度;其中,所述对象识别模型为训练得到的用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的识别模型;所述第一时间段为第二时间段的前一指定时间段;对象筛选单元,用于根据各初选对象在所述第二时间段的对象使用热度,从初选对象中选取至少一个商品对象作为所述第二时间段内、与所述商品对象主题词相匹配的商品对象。
- 一种商品对象选取装置,其特征在于,包括:主题词接收单元,用于接收用户输入的商品对象主题词;主题词发送单元,用于将所述商品对象主题词发送至服务器;对象信息接收单元,用于接收所述服务器根据所述商品对象主题词返回的 商品对象信息;对象确定单元,用于将所述商品对象信息所对应的商品对象确定为第二时间段内、与所述商品对象主题词相匹配的商品对象;其中,所述商品对象信息是所述服务器根据设定的对象识别模型、以及与所述商品对象主题词相匹配的各初选对象在第一时间段内的使用特征数据,从与所述商品对象主题词相匹配的初选对象中所选取的商品对象的相关信息;所述第一时间段为第二时间段的前一指定时间段;所述设定的对象识别模型用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系。
- 一种模型确定装置,其特征在于,包括:数据接收单元,用于接收用户终端发送的对象识别模型训练样本数据,其中,所述对象识别模型训练样本数据中包含各对象识别模型样本对象的基础特征数据,且,每一对象识别模型样本对象的基础特征数据包括所述对象识别模型样本对象在各第三时间段内的使用特征数据,以及所述对象识别模型样本对象在各对应的第四时间段的对象使用热度;所述第三时间段为对应的第四时间段的前一指定时间段;模型训练单元,用于根据所述对象识别模型训练样本数据中包含的各对象识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的初始对象识别模型进行训练,得到用于表征商品对象在第一时间段内的使用特征数据、与商品对象在第二时间段的对象使用热度之间的关联关系的对象识别模型。
- 一种模型确定装置,其特征在于,包括:数据接收单元,用于接收用户终端发送的类目识别模型训练样本数据,其中,所述类目识别模型训练样本数据中包含各类目识别模型样本对象的基础特征数据,且,每一类目识别模型样本对象的基础特征数据包括所述类目识别模 型样本对象对应的类目在各第一时间段的类目使用热度,以及所述类目识别模型样本对象对应的类目在与各第一时间段相对应的一个或多个历史同期时间段的类目使用热度;模型训练单元,用于根据所述类目识别模型训练样本数据中包含的各类目识别模型样本对象的基础特征数据,对预先建立的用于预测商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的初始类目识别模型进行训练,得到用于表征商品对象类目在第二时间段的类目使用热度、与商品对象类目在与该第二时间段相对应的一个或多个历史同期时间段的类目使用热度之间的关联关系的类目识别模型。
- 一种使用热度确定装置,其特征在于,包括:数据获取单元,用于获取各商品对象在第一时间段内的使用特征数据;热度确定单元,用于基于商品对象在第一时间段内的使用特征数据与商品对象在第二时间段的对象使用热度之间的关联关系,以及,各商品对象在第一时间段内的使用特征数据,确定各商品对象在第二时间段的对象使用热度;所述第一时间段为第二时间段的前一指定时间段;且,所述关联关系是根据各样本对象在各第三时间段的使用特征数据以及各样本对象在各对应的第四时间段的对象使用热度所建立的;所述第三时间段为对应的第四时间段的前一指定时间段。
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