CN113743972B - Method and device for generating article information - Google Patents

Method and device for generating article information Download PDF

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CN113743972B
CN113743972B CN202010826102.4A CN202010826102A CN113743972B CN 113743972 B CN113743972 B CN 113743972B CN 202010826102 A CN202010826102 A CN 202010826102A CN 113743972 B CN113743972 B CN 113743972B
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characteristic value
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CN113743972A (en
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潘扬
张青青
毛锐
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Beijing Wodong Tianjun Information Technology Co Ltd
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Abstract

The invention discloses a method and a device for generating article information, and relates to the technical field of computers. One embodiment of the method comprises the following steps: the basic score of the article can be calculated based on a back propagation algorithm model by acquiring multidimensional characteristic data of the article to be promoted; calculating the grade score of the article provider based on the classification model by acquiring the multidimensional characteristics of the article provider; further calculating the advertisement score of the article according to the basic score of the article, the grade score of the provider and the weight value of the article, generating article information to be promoted, and sending the article information; the client generates an article promotion page according to the article information; by combining the feature data of the articles and the article providers with the multi-dimensional dynamic feature data, the accuracy of calculating and selecting the promoted articles is improved, and the conversion effect of promotion is further improved; and the scores of the articles and the providers are calculated through different models, and the promotion scores are obtained, so that the accuracy of generating the information of the articles to be promoted is improved.

Description

Method and device for generating article information
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for generating article information.
Background
Currently, when an object provider promotes an object, the promotion effect is often evaluated according to the sales condition of the object, for example, an advertisement form of charging according to the order quantity is usually adopted in the field of electronic commerce; in the service industry, the advertisement mode focuses on the advertisement conversion effect, in order to improve the advertisement conversion effect, the advertisement conversion effect of the article needs to be evaluated according to the relevant characteristics of the article, so that a proper popularization article is selected, and in the existing evaluation advertisement conversion model, the commonly selected article characteristic index usually comprises static characteristics; and the advertisement conversion model is generally obtained by training the characteristics of the article by adopting a single model such as a logistic regression model or a neural network model. In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
In the existing advertisement conversion model, when the characteristics of the model are selected, the static values of the characteristics are usually selected to determine popularization objects, and the accuracy of selecting advertisement objects is low due to the fact that the dynamic characteristic values of the objects are not considered, so that the advertisement conversion effect is low. In addition, the existing advertisement conversion model is generally trained by adopting a single model, and when the characteristics of objects are more, the single model is difficult to deal with the characteristics of the objects with multiple dimensions and complex scenes, so that the complexity of generating the information of the objects to be promoted is improved and the calculation accuracy is reduced.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method and a device for generating article information, which can calculate the basic score of an article based on a back propagation algorithm model by acquiring multidimensional characteristic data of the article to be promoted; calculating the grade score of the article provider based on the classification model by acquiring the multidimensional characteristics of the article provider; further calculating the advertisement score of the article according to the basic score of the article, the grade score of the provider and the weight value of the article, generating article information to be promoted, and sending the article information; the client generates an article promotion page according to the article information; by combining the feature data of the articles and the article providers with the multi-dimensional dynamic feature data, the accuracy of calculating and selecting the promoted articles is improved, and the conversion effect of promotion is further improved; and the scores of the articles and the providers are calculated through different models, and the promotion scores are obtained, so that the accuracy of generating the information of the articles to be promoted is improved.
To achieve the above object, according to one aspect of the embodiments of the present invention, there is provided a method of generating article information, including: acquiring the category of an article to be promoted, and calculating the basic score of the article to be promoted based on a back propagation algorithm model according to the feature data of the article contained in the category and the feature data of the article to be promoted; acquiring characteristics of an article provider of an article to be promoted, and calculating a grade value of the article provider based on a classification model according to the characteristics of the article contained in the category and the characteristics of the article provider; determining the promotion score of the article to be promoted according to the basic score of the article to be promoted and the grade value of the article provider; and ordering based on the promotion scores to form a corresponding article sequence to be promoted, acquiring a set number of articles from the article sequence to be promoted to generate corresponding article information, and sending the article information.
Optionally, the method for generating article information is characterized in that,
The feature data of the article to be promoted comprises: the current feature value, the first dimension feature value and the second dimension feature value; and inputting the current characteristic value, the first dimension characteristic value and the second dimension characteristic value into a back propagation algorithm model to obtain the basic score of the article to be promoted.
Optionally, the method for generating article information is characterized in that,
And obtaining the category of the article to be promoted, calculating the characteristic average value of the article contained in the category in a set historical time range, and calculating the first dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
Optionally, the method for generating article information is characterized in that,
Calculating a characteristic statistical value of the article to be promoted within a set time, and calculating the second dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
Optionally, the method for generating article information is characterized in that,
The features of the item provider include: providing a provider characteristic value, a first dimension provider characteristic value and a second dimension provider characteristic value; and inputting the provider characteristic value, the first dimension provider characteristic value and the second dimension provider characteristic value into the classification model to obtain the grade value of the article provider.
Optionally, the method for generating article information is characterized in that,
And obtaining the category of the article provider, calculating the provider characteristic value statistic value of the article provider corresponding to the category in a set historical time range, and calculating the first dimension provider characteristic value based on the provider characteristic value of the article provider and the statistic value of the provider characteristic value.
Optionally, the method for generating article information is characterized in that,
And calculating the statistical value of the characteristic value of the provider of the article provider in the set time, and calculating the statistical value of the second dimension provider based on the characteristic value of the provider of the article provider and the statistical value of the characteristic value of the provider.
Optionally, the method for generating article information is characterized in that,
Acquiring an abnormal characteristic value of the article to be promoted by using an abnormal value detection model, and inputting the abnormal characteristic value into a convolutional neural network model to acquire a weight value of the article to be promoted; and calculating the promotion score of the article to be promoted according to the basic score of the article to be promoted, the grade value of the article provider and the weight value of the article to be promoted.
To achieve the above object, according to a second aspect of the embodiments of the present invention, there is provided a method of generating article information, including: receiving the one or more item information, the item information comprising a promotion score; and determining the display position of the corresponding object on the page according to the promotion score.
In order to achieve the above object, according to a third aspect of the embodiments of the present invention, there is provided an apparatus for article information, comprising: the system comprises an article score calculating module, a popularization score calculating module and an article information generating module; wherein,
The article score calculating module is used for obtaining the category of the article to be promoted, calculating the basic score of the article to be promoted based on a back propagation algorithm model according to the characteristic data of the article contained in the category and the characteristic data of the article to be promoted; acquiring characteristics of an article provider of an article to be promoted, and calculating a grade value of the article provider based on a classification model according to the characteristics of the article contained in the category and the characteristics of the article provider;
The promotion score calculating module is used for determining the promotion score of the article to be promoted according to the basic score of the article to be promoted and the grade value of the article provider;
and the article information generation module is used for ordering and forming a corresponding article sequence to be promoted based on the promotion scores, acquiring a set number of articles from the article sequence to be promoted to generate corresponding article information, and sending the article information.
Optionally, the device for generating article information is characterized in that,
The feature data of the article to be promoted comprises: the current feature value, the first dimension feature value and the second dimension feature value; and inputting the current characteristic value, the first dimension characteristic value and the second dimension characteristic value into a back propagation algorithm model to obtain the basic score of the article to be promoted.
Optionally, the device for generating article information is characterized in that,
And obtaining the category of the article to be promoted, calculating the characteristic average value of the article contained in the category in a set historical time range, and calculating the first dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
Optionally, the device for generating article information is characterized in that,
Calculating a characteristic statistical value of the article to be promoted within a set time, and calculating the second dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
Optionally, the device for generating article information is characterized in that,
The features of the item provider include: providing a provider characteristic value, a first dimension provider characteristic value and a second dimension provider characteristic value; and inputting the provider characteristic value, the first dimension provider characteristic value and the second dimension provider characteristic value into the classification model to obtain the grade value of the article provider.
Optionally, the device for generating article information is characterized in that,
And obtaining the category of the article provider, calculating the provider characteristic value statistic value of the article provider corresponding to the category in a set historical time range, and calculating the first dimension provider characteristic value based on the provider characteristic value of the article provider and the statistic value of the provider characteristic value.
Optionally, the device for generating article information is characterized in that,
And calculating the statistical value of the characteristic value of the provider of the article provider in the set time, and calculating the statistical value of the second dimension provider based on the characteristic value of the provider of the article provider and the statistical value of the characteristic value of the provider.
Optionally, the device for generating article information is characterized in that,
Acquiring an abnormal characteristic value of the article to be promoted by using an abnormal value detection model, and inputting the abnormal characteristic value into a convolutional neural network model to acquire a weight value of the article to be promoted; and calculating the promotion score of the article to be promoted according to the basic score of the article to be promoted, the grade value of the article provider and the weight value of the article to be promoted.
In order to achieve the above object, according to a fourth aspect of an embodiment of the present invention, there is provided an apparatus for article information, comprising: generating a page module; the generation page module is used for receiving one or more item information, wherein the item information comprises a promotion score; and determining the display position of the corresponding object on the page according to the promotion score.
In order to achieve the above object, according to a fifth aspect of an embodiment of the present invention, there is provided an electronic device that generates article information, comprising: one or more processors; and a storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of generating item information as described in any of the methods of generating item information above.
To achieve the above object, according to a sixth aspect of the embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a method as described in any one of the above methods of generating item information.
One embodiment of the above invention has the following advantages or benefits: the basic score of the article can be calculated based on a back propagation algorithm model by acquiring multidimensional characteristic data of the article to be promoted; calculating the grade score of the article provider based on the classification model by acquiring the multidimensional characteristics of the article provider; further calculating the advertisement score of the article according to the basic score of the article, the grade score of the provider and the weight value of the article, generating article information to be promoted, and sending the article information; the client generates an article promotion page according to the article information; by combining the feature data of the articles and the article providers with the multi-dimensional dynamic feature data, the accuracy of calculating and selecting the promoted articles is improved, and the conversion effect of promotion is further improved; and the scores of the articles and the providers are calculated through different models, and the promotion scores are obtained, so that the accuracy of generating the information of the articles to be promoted is improved.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a flow chart of a method of generating item information according to one embodiment of the present invention;
FIG. 2 is a flow chart of a method for obtaining a base score for an item according to one embodiment of the present invention;
FIG. 3 is a flow chart of a method for obtaining item provider rank scores according to one embodiment of the present invention;
FIG. 4 is a schematic structural view of an apparatus for generating article information according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an apparatus for receiving information about an article according to an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
Fig. 7 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, an embodiment of the present invention provides a method for generating item information, which may include the steps of:
Step S101: acquiring the category of an article to be promoted, and calculating the basic score of the article to be promoted based on a back propagation algorithm model according to the feature data of the article contained in the category and the feature data of the article to be promoted; and acquiring the characteristics of the article provider of the article to be promoted, and calculating the grade value of the article provider based on the classification model according to the characteristics of the article contained in the category and the characteristics of the article provider.
Specifically, when determining the article to be promoted, firstly acquiring the category of the article, namely acquiring the category of the article to be promoted, for example, the article to be promoted can be a dish of a restaurant, a product of a travel agency, a commodity of an electronic mall and the like, wherein the category of the article is described below by taking the commodity of the electronic mall as an example, the commodity classification in the electronic mall such as a tool book in a book, flour in food, a television in a household appliance, basketball in sports, a tent in the open air and the like, and further, the characteristic data of the article to be promoted and the characteristic data of the article to be promoted are included; wherein the feature data includes: the current feature value, the first dimension feature value and the second dimension feature value; the current characteristic value may be current sales information of the article to be promoted, and taking the article as an example, the current sales information of the article may be current price, recent sales volume, current commission, current preferential price, and the like of the article; the first dimension characteristic value is calculated by the current information of the article to be promoted and the article information of the same category, for example: calculating an average value of prices of all articles in the category in the past 30 days (namely, a set historical time range), wherein the average value is an example of characteristic statistical values of the articles in the set historical time range, and it is understood that the statistical values can be average values, median values, percentile values and the like; further, dividing the current price (namely the current characteristic value) of the article to be promoted by the statistical value to obtain a first dimension characteristic value; it can be understood that the calculation method for calculating the first dimension feature value may be division, taking a statistic deviation, or the like, that is, obtaining a category of the article to be promoted, calculating a feature average value of the article included in the category in a set historical time range, and calculating the first dimension feature value based on the current feature value and the feature statistic of the article to be promoted. The second dimension characteristic value is calculated through the current information of the article to be promoted and the history information of the article to be promoted, for example: calculating the current price (namely the current characteristic value) of the article to be promoted divided by the average price (namely the characteristic statistical value) in the past 15 days (namely the set time) to be used as a second dimension characteristic value; it can be understood that the operation method for calculating the second dimension characteristic value can be division, statistics value deviation and the like; namely, calculating the characteristic statistical value of the article to be promoted in the set time, and calculating the second dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
Further, calculating the basic score of the article to be promoted based on a back propagation algorithm model according to the current characteristic value, the first dimension characteristic value and the second dimension characteristic value; the description of the back propagation algorithm model to calculate the basic score of the item to be promoted is consistent with steps S201-S203, and will not be described herein.
Further, acquiring characteristics of an article provider of the article to be promoted; the features of the item provider include: providing a provider characteristic value, a first dimension provider characteristic value and a second dimension provider characteristic value; the provider characteristic value is service information of the provider, for example: a provider good score, a provider after-sales score, a provider logistic score, etc.; the first dimension provider feature value is calculated by the current information of the item provider and the information of all item providers in the same category, for example: calculating the average value (namely the characteristic value statistic value of the provider) of the good scores of all the item providers in the category in the past 30 days (namely the set historical time range); dividing the good score value (namely the provider characteristic value) of the provider where the article to be promoted is located by the statistical value to obtain a first dimension provider characteristic value; it can be understood that the operation method for calculating the feature value of the first dimension provider can be division, statistics value deviation and the like; the category of the associated merchant is obtained, a provider characteristic value statistic value of the category corresponding to the provider in a set historical time range is calculated, and the first dimension provider characteristic value is calculated based on the provider characteristic value of the article provider and the provider characteristic value statistic value.
The second dimension provider characteristic value is calculated by the current information of the provider and the history information of the provider, for example: calculating the logistic score (namely the characteristic value of the provider) of the provider divided by the average logistic score (namely the characteristic value statistical value of the provider) in the past 7 days (namely the set time) as the characteristic value of the provider of the second dimension; it can be understood that the operation method for calculating the feature value of the second dimension provider can be division, statistics value deviation and the like; the provider characteristic value statistical value of the merchant to be promoted in the set time is calculated, and the second dimension provider characteristic value is calculated based on the provider characteristic value of the article provider and the provider characteristic value statistical value.
Further, the provider characteristic value, the first dimension provider characteristic value and the second dimension provider characteristic value are input into the classification model, and the grade value of the article provider is obtained. The description of calculating the rank value of the provider based on the classification model is consistent with steps S301-S303, and will not be repeated here.
It can be understood that the specific content of the feature and the feature value of the article to be promoted, the specific calculating method of the first dimension feature value and the second dimension feature value, the specific content of the feature and the feature value of the article provider, the specific calculating method of the first dimension provider feature value and the second dimension provider feature value, the specific calculating method of the setting history time range, the setting time and the statistical value are set according to the industry, the service scene and the service requirement of the article provider, and the invention is not limited to the above.
Step S102: and determining the promotion score of the article to be promoted according to the basic score of the article to be promoted and the grade value of the article provider.
Specifically, according to the description of step S101, a basic score of the item to be promoted and a grade value of the item provider are obtained, and further, a promotion score of the item to be promoted is determined based on the basic score of the item to be promoted and the grade value of the item provider.
For example: using formula (1) as an example, a promotion score is calculated as follows:
ComprehensiveScore＝ShopScore*BaseScore(1)
Wherein BaseScore is indicated as the base score of the item to be promoted; shopScore is a rating value for the item provider; "x" indicates a multiplication symbol, comprehensiveScore indicates an item provider score for the item to be promoted; namely, calculating the basic score of the article to be promoted and the grade value of the article provider, and determining the promotion score of the article to be promoted; the invention is not limited to a specific formula for calculating the advertisement score of the article to be promoted based on the basic score of the article to be promoted and the grade value of the article provider.
Further, another embodiment of determining the promotion score is: acquiring an abnormal characteristic value of the article to be promoted by using an abnormal value detection model, and inputting the abnormal characteristic value into a convolutional neural network model to acquire a weight value of the article to be promoted; and calculating the promotion score of the article to be promoted according to the basic score of the article to be promoted, the grade value of the article provider and the weight value of the article to be promoted.
Specifically, for example: in a sales promotion such as a second killing of an item or a large sales promotion force, an abnormal characteristic value may occur to some items, for example, a commission is greatly increased or a price is greatly reduced, and advertisements of the part of the items can be generally converted to be high, so that the goods with the commission increased or the price reduced can be detected in advance by using an abnormal characteristic value detection model, that is, based on static characteristics and dynamic characteristics of the commission and the price, the abnormal characteristic value such as a commission change, a price change or a preferential force change is identified in advance (for example, one day in advance), further, the corresponding item is acquired, a convolutional neural network model is adopted, the weight value of the part of the items is determined, the weight value is set to be a value greater than 1, and the weight value of the item with no detected abnormal value is set to be 1. Through the step, the characteristic value of the article can be dynamically obtained, and the promotion score of the article to be promoted can be obtained more accurately.
Further, according to the basic score of the article to be promoted, the grade value of the article provider and the weight value of the article to be promoted, calculating the promotion score of the article to be promoted, taking the following formula as an example:
ComprehensiveScore＝ShopScore*BaseScore*WeightScore
Wherein BaseScore is indicated as the base score of the item to be promoted; shopScore is a rating value for the item provider; weightScore indicates a weight value of the article to be promoted; "x" indicates a multiplication symbol, comprehensiveScore indicates an advertising score for the item to be promoted; namely, an abnormal value detection model is utilized to obtain an abnormal characteristic value of the article to be promoted, and the abnormal characteristic value is input into a convolutional neural network model to obtain a weight value of the article to be promoted; and calculating the promotion score of the article to be promoted according to the basic score of the article to be promoted, the grade value of the article provider and the weight value of the article to be promoted. The invention is not limited to a specific formula for calculating the promotion score of the article to be promoted based on the basic score of the article to be promoted, the grade value of the provider of the associated article and the weight value.
Step S103: and ordering based on the promotion scores to form a corresponding article sequence to be promoted, acquiring a set number of articles from the article sequence to be promoted to generate corresponding article information, and sending the article information.
Specifically, according to the description of step S102, promotion scores of a plurality of items to be promoted are obtained through calculation; further, ranking the promotion scores, e.g., from high to low; and obtaining the articles corresponding to the promotion scores of the set quantity from the high to the first as promotion commodities. It will be appreciated that one or more items to be promoted in the order may be attributed to the same item provider or may be attributed to different item providers (e.g., a certain business consortium advertising, or a certain company advertising for a plurality of businesses under a flag); further, acquiring a set number of articles from the article sequence to be promoted to generate corresponding article information, and sending the article information to a client. It will be appreciated that the set number is determined based on the advertisement design, for example: the set number can be 1, the set number can be 50, and the set number of the advertisement commodities which can be displayed is related to the content, the form, the duration, the display mode and other factors of the advertisement displayed and displayed on the interface; further, the client determines the display position of the item on the page according to the promotion score contained in the received item information, namely, receives the one or more item information, wherein the item information contains the promotion score; and determining the display position of the corresponding object on the page according to the promotion score. The invention does not limit the specific numerical value of the set quantity, the specific content corresponding to the promoted article, the display mode and the specific content of the interface.
As shown in FIG. 2, an embodiment of the present invention provides a method of obtaining a basic score for an advertisement item, the method may include the steps of:
step S201: acquiring the category of an article to be promoted, and calculating the basic score of the article to be promoted based on a back propagation algorithm model according to the feature data of the article contained in the category and the feature data of the article to be promoted; the feature data of the commodity to be promoted comprises: current eigenvalue, first dimension eigenvalue, second dimension eigenvalue.
Specifically, regarding to acquiring the category of the to-be-promoted item, the description of the feature data of the item and the feature data of the to-be-promoted item according to the category is consistent with step S101, and will not be described herein again; further, the basic score of the item to be promoted is calculated based on the back propagation algorithm model, and the following illustrates the step of calculating the basic score of the item to be promoted based on the back propagation algorithm model: specifically, the feature data of the article to be promoted includes: the present feature value, the first dimension feature value, the second dimension feature value, for example, the commission proportion, the price and the preferential price of the article to be promoted are obtained as the present feature value, and the first dimension feature value and the second dimension feature value are calculated based on the present feature values; and training the characteristic values of the to-be-promoted items by adopting a back propagation algorithm model, and obtaining the basic scores of the to-be-promoted items. It will be appreciated that the commission, commission proportion, price and preferential price are the basic features for determining the conversion effect of the advertisement, so that the feature information engineering construction of the article is performed according to the commission, commission proportion, price and preferential price, and the like, the training is performed by adopting a back propagation algorithm model, further, in order to prevent the back propagation algorithm model from being over-fitted, a regularization strategy can be adopted, and the square sum of the connection weight and the threshold value is added in the error objective functionThis term describes the complexity of the network, thereby avoiding overcomplicating the back propagation algorithm model too complex. The error objective function of the back propagation algorithm model after the "regularization" strategy is shown in equation (2).
Wherein the method comprises the steps ofRepresenting the error of the network at (x k,yk); w i represents the connection weight and the threshold; λ and 1- λ represent weights for empirical error and network complexity, respectively. Wherein λ e (0, 1) is used to compromise both the empirical error and the network complexity, preferably, λ is set to 0.7 according to the test.
Step S202: and obtaining the category of the article to be promoted, calculating a characteristic statistical value of the article contained in the category in a set historical time range, and calculating the first dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
Specifically, a method of calculating the first dimension characteristic value is described below with an example shown in table 1: taking the price of the to-be-promoted item as an example, acquiring the current characteristic value (for example, the current price) of the to-be-promoted item, further calculating the characteristic statistical value of the item contained in the corresponding category in a set historical time range, for example, calculating the average price (namely, the characteristic statistical value) of all items in the category in the past 30 days (namely, in the set historical time range), dividing the current price by the average price of all items, and calculating to obtain a first dimension characteristic value; for example: the current price of the flour A is 110 yuan, the price of the flour in the same class (comprising the same weight) in the past 30 days is calculated to be 100 yuan, and the price is calculated to be 1.1 by dividing 110 by 100, namely the first dimension characteristic value of the price; as shown in table 1, similarly to the price, according to the current characteristic value: the commission, the commission proportion, the preferential price and the sales calculate corresponding first dimension characteristic values.
Table 1 example of calculating eigenvalues of items to be promoted
Step S203: calculating a characteristic statistical value of the article to be promoted within a set time, and calculating the second dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
Specifically, the method of calculating the second dimension characteristic value is still described with an example shown in table 1: taking the price of the article to be promoted as an example, acquiring the current characteristic value (such as the current price) of the article to be promoted, further calculating the characteristic statistical value of the article to be promoted within a set time, for example, calculating the average price (namely the example of the characteristic statistical value) of the article to be promoted within the past 7 days (namely the set time), and further dividing the current price by the average price (namely the characteristic statistical value), so as to calculate a second dimension characteristic value; for example: the current price of the flour A is 110 yuan, the price of the flour A in the past 7 days is calculated to be 120 yuan, and the calculated value is calculated to be divided by 120 to obtain 0.92, namely a second dimension characteristic value corresponding to the price; as shown in table 1, similarly to the price, according to the current characteristic value: the commission, the commission proportion, the preferential price and the sales calculate corresponding second dimension characteristic values.
As shown in fig. 3, an embodiment of the present invention provides a method for obtaining a rank score of an item provider, which may include the steps of:
Step S301: acquiring characteristics of an article provider of an article to be promoted, and calculating a grade value of the article provider based on a classification model according to the characteristics of the article provider contained in the category; the features of the item provider include: the features of the item provider include: provider characteristic value, first dimension provider characteristic value, second dimension provider characteristic value.
Specifically, regarding the obtaining of the category of the item to be promoted, the description of the obtaining of the category including the characteristics of the item provider is consistent with step S101, and will not be described here again; further, the step of calculating the rank value of the item provider based on the classification model is exemplified below, for example: using a softmax model as a classification model, a cost function formula corresponding to the softmax model is shown as formula (3):
Wherein m is indicated as the number of samples; k indicates the number of classifications (e.g., set the rank value of the item provider to five rank values of top, middle, bottom, etc., so k=5); 1{ · } is indicated as an indirection function; y is indicated as a label, in this example as an item provider rating value; x indicates a provider characteristic value, a first dimension provider characteristic value, a second dimension provider characteristic value, for example, taking 9 characteristic values shown in table 2 contained in step S302 as an example; θ is indicated as softmax model parameter vector; λ is indicated as the weight decay coefficient; the description of calculating the corresponding first dimension provider characteristic value and second dimension provider characteristic value based on the provider characteristic value (e.g., good score, after-sale score, logistics score) is consistent with steps S302-S303, and will not be repeated here; further, in the present embodiment, the ranks of the provider are set to be upper, middle, lower, and lower, etc. (5 ranks in total), that is, k=5, and the rank values corresponding to the ranks are set to be 1.4,1.2,1.0,0.8,0.6, respectively.
Alternatively, the softmax model formula described above may be solved using a gradient descent method, which requires solving the derivative of the cost function, which may be as follows:
After solving to obtain a specific softmax model, the probability of the provider grade can be predicted, and then the grade score of the provider is determined according to the following example formula:
The invention is not limited to the classification model used to determine the provider rank scores and the specific formulas used.
Step S302: and acquiring the characteristics of the article provider, calculating the provider characteristic value statistic value of the merchant corresponding to the category in a set historical time range, and calculating the first dimension provider characteristic value based on the provider characteristic value and the provider characteristic value statistic value of the associated provider.
Specifically, a method of calculating the first dimension provider characteristic value is described below with an example shown in table 2: taking the good score of the provider as an example, acquiring a provider characteristic value (such as the good score) of the article to be promoted, further calculating a characteristic statistical value of the provider contained in the corresponding category in a set historical time range, for example, calculating an average value (namely the provider characteristic value statistical value) of the good scores of all the providers in the category in the past 30 days (namely the set historical time range), dividing the good score by the average value, and calculating a first dimension provider characteristic value corresponding to the good score of the provider; for example: the score value of the provider A is 4.9, the average value of the score values (namely the characteristic statistical value) of the class provider in the past 30 days is calculated to be 4, and the score value is calculated to be 4.9 divided by 4 to obtain 1.23, namely the characteristic value of the first dimension provider of the score value; as shown in table 2, similar to the good score value, according to the provider characteristic value: and calculating corresponding first dimension characteristic values according to the after-sales scores and the logistics scores. That is, the first dimension provider characteristic value is calculated based on the provider characteristic value and the provider characteristic value statistic of the item provider.
Table 2 calculates feature value examples for item providers
Step S303: and calculating a provider characteristic value statistic value of the article provider in a set time, and calculating the second dimension provider characteristic value based on the provider characteristic value of the article provider and the provider characteristic value statistic value.
Specifically, the method of calculating the second dimension provider feature value is still described with the example shown in table 2: taking the good score of the article provider as an example, acquiring the article provider (such as the good score) of the article to be promoted, further calculating the provider characteristic value statistics value of the provider contained in the corresponding category within a set time, for example, calculating the average value (namely the provider characteristic value statistics value) of the good score of the provider within the past 7 days (namely within the set time), dividing the good score by the average value, and calculating the second dimension provider characteristic value corresponding to the provider good score; for example: the good score of the provider A is 4.9, the average good score in the past 7 days is calculated to be 4, and the value obtained by dividing 4 by 4 is calculated to obtain 1.23, namely the second dimension provider characteristic value of the good score; as shown in table 2, similar to the good score value, according to the provider characteristic value: and calculating corresponding second dimension provider characteristic values according to the after-sales scores and the logistics scores. That is, the second dimension provider characteristic value is calculated based on the provider characteristic value and the provider characteristic value statistic of the article provider.
As shown in fig. 4, an embodiment of the present invention provides an apparatus 400 for generating article information, including: a calculate item score module 401, a calculate promotion score module 402, and a generate item information module 403; wherein,
The item score calculating module 401 is configured to obtain a category of an item to be promoted, and calculate a basic score of the item to be promoted based on a back propagation algorithm model according to feature data of the item and feature data of the item to be promoted included in the category; acquiring characteristics of an article provider of an article to be promoted, and calculating a grade value of the article provider based on a classification model according to the characteristics of the article contained in the category and the characteristics of the article provider;
the promotion score calculating module 402 determines a promotion score of the to-be-promoted item according to the basic score of the to-be-promoted item and the grade value of the item provider;
The article information generating module 403 sorts and forms a corresponding article sequence to be promoted based on the promotion scores, acquires a set number of articles from the article sequence to be promoted to generate corresponding article information, and sends the article information.
Optionally, the module 401 for calculating the item score includes: the current feature value, the first dimension feature value and the second dimension feature value; and inputting the current characteristic value, the first dimension characteristic value and the second dimension characteristic value into a back propagation algorithm model to obtain the basic score of the article to be promoted.
Optionally, the item score calculating module 401 is configured to obtain a category of the item to be promoted, calculate a feature statistic of the item included in the category within a set historical time range, and calculate the first dimension feature value based on the current feature value and the feature statistic of the item to be promoted.
Optionally, the item score calculating module 401 is configured to calculate a feature statistic value of the item to be promoted within a set time, and calculate the second dimension feature value based on the current feature value and the feature statistic value of the item to be promoted.
Optionally, the calculating item score module 401 includes features of the item provider including: providing a provider characteristic value, a first dimension provider characteristic value and a second dimension provider characteristic value; and inputting the provider characteristic value, the first dimension provider characteristic value and the second dimension provider characteristic value into the classification model to obtain the grade value of the article provider.
Optionally, the module 401 for calculating an item score is configured to obtain a category of the item provider, calculate a provider feature value statistic of the category corresponding to the provider within a set historical time range, and calculate the first dimension provider feature value based on the provider feature value of the item provider and the provider feature value statistic.
Optionally, the module 401 for calculating an item score is configured to calculate a statistics value of a provider feature value of the item provider in a set time, and calculate the second dimension provider statistics value based on the provider feature value of the item provider and the statistics value of the provider feature value.
Optionally, the promotion score calculating module 402 is configured to obtain an abnormal characteristic value of the article to be promoted by using an abnormal value detection model, and input the abnormal characteristic value into a convolutional neural network model to obtain a weight value of the article to be promoted; and calculating the promotion score of the article to be promoted according to the basic score of the article to be promoted, the grade value of the article provider and the weight value of the article to be promoted.
As shown in fig. 5, an embodiment of the present invention provides an apparatus 500 for generating article information, including: generating a page module 501; wherein, the generating page module 501 is configured to receive one or more item information, where the item information includes a promotion score; and determining the display position of the corresponding object on the page according to the promotion score.
The embodiment of the invention also provides electronic equipment for generating the article information, which comprises: one or more processors; and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method provided by any of the embodiments described above.
The embodiment of the invention also provides a computer readable medium, on which a computer program is stored, which when executed by a processor implements the method provided by any of the above embodiments.
Fig. 6 illustrates an exemplary system architecture 600 of a method of item information or an apparatus of item information to which embodiments of the present invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 is used as a medium to provide communication links between the terminal devices 601, 602, 603 and the server 605. The network 604 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 605 via the network 604 using the terminal devices 601, 602, 603 to receive or send messages, etc. Various client applications such as a web browser application, a search class application, an instant messaging tool, a mailbox client, and the like may be installed on the terminal devices 601, 602, 603.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 605 may be a server providing various services, for example, the server transmits the generated item information to the terminal devices 601, 602, 603, and the terminal devices determine the display position of the item according to the received item information and generate a page.
It should be noted that, the method for generating the article information provided in the embodiment of the present invention is generally executed by the server 605, and accordingly, the device for generating the article information is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, there is illustrated a schematic diagram of a computer system 700 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the system 700 are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 701.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules and/or units involved in the embodiments of the present invention may be implemented in software, or may be implemented in hardware. The described modules and/or units may also be provided in a processor, e.g., may be described as: a processor includes a calculate item score module, a calculate promotion score module, and a generate page information module. The names of these modules do not limit the module itself in some cases, for example, the module for calculating a promotion score may also be described as "a module for determining a promotion score of an item to be promoted according to a base score of the item to be promoted and a rank score of an item provider".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: acquiring the category of an article to be promoted, and calculating the basic score of the article to be promoted based on a back propagation algorithm model according to the feature data of the article contained in the category and the feature data of the article to be promoted; acquiring characteristics of an article provider of an article to be promoted, and calculating a grade value of the article provider based on a classification model according to the characteristics of the article contained in the category and the characteristics of the article provider; determining the promotion score of the article to be promoted according to the basic score of the article to be promoted and the grade value of the article provider; and ordering based on the promotion scores to form a corresponding article sequence to be promoted, acquiring a set number of articles from the article sequence to be promoted to generate corresponding article information, and sending the article information. Receiving one or more item information, the item information comprising a promotion score; and determining the display position of the corresponding object on the page according to the promotion score.
According to the technical scheme provided by the embodiment of the invention, the basic score of the article can be calculated based on the back propagation algorithm model by acquiring the multidimensional characteristic data of the article to be promoted; calculating the grade score of the article provider based on the classification model by acquiring the multidimensional characteristics of the article provider; further calculating the advertisement score of the article according to the basic score of the article, the grade score of the provider and the weight value of the article, generating article information to be promoted, and sending the article information; the client generates an article promotion page according to the article information; by combining the feature data of the articles and the article providers with the multi-dimensional dynamic feature data, the accuracy of calculating and selecting the promoted articles is improved, and the conversion effect of promotion is further improved; and the scores of the articles and the providers are calculated through different models, and the promotion scores are obtained, so that the accuracy of generating the information of the articles to be promoted is improved.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (12)

1. A method of generating item information, comprising:
Acquiring the category of the article to be promoted, wherein the category comprises characteristic data of the article and the characteristic data of the article to be promoted; wherein, the characteristic data of the article to be promoted comprises: the current feature value, the first dimension feature value and the second dimension feature value;
the current characteristic value is the current sales information of the article to be promoted;
The first dimension characteristic value is calculated by the current information of the article to be promoted and the article information of the same category;
the second dimension characteristic value is calculated by the current information of the article to be promoted and the history information of the article to be promoted;
inputting the current characteristic value, the first dimension characteristic value and the second dimension characteristic value into a back propagation algorithm model to obtain a basic score of the article to be promoted;
Acquiring characteristics of an article provider of an article to be promoted, and calculating a grade value of the article provider based on a classification model according to the characteristics of the article contained in the category and the characteristics of the article provider; wherein the characteristics of the item provider include: providing a provider characteristic value, a first dimension provider characteristic value and a second dimension provider characteristic value;
The provider characteristic value is service information of the provider;
the characteristic value of the first dimension provider is calculated by the current information of the article provider and the information of all the article providers in the same category;
The second dimension provider characteristic value is calculated by the current information of the provider and the history information of the provider;
Determining the promotion score of the article to be promoted according to the basic score of the article to be promoted and the grade value of the article provider;
and ordering based on the promotion scores to form a corresponding article sequence to be promoted, acquiring a set number of articles from the article sequence to be promoted to generate corresponding article information, and sending the article information.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
And obtaining the category of the article to be promoted, calculating the characteristic average value of the article contained in the category in a set historical time range, and calculating the first dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
Calculating a characteristic statistical value of the article to be promoted within a set time, and calculating the second dimension characteristic value based on the current characteristic value and the characteristic statistical value of the article to be promoted.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,
The features of the item provider include: providing a provider characteristic value, a first dimension provider characteristic value and a second dimension provider characteristic value;
The provider characteristic value is service information of the provider;
the characteristic value of the first dimension provider is calculated by the current information of the article provider and the information of all the article providers in the same category;
The second dimension provider characteristic value is calculated by the current information of the provider and the history information of the provider;
And inputting the provider characteristic value, the first dimension provider characteristic value and the second dimension provider characteristic value into the classification model to obtain the grade value of the article provider.
5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,
Obtaining the category of the article provider, and calculating the provider characteristic value statistic value of the article provider corresponding to the category in a set historical time range, wherein the characteristics of the article provider comprise: providing a provider characteristic value, a first dimension provider characteristic value and a second dimension provider characteristic value;
The provider characteristic value is service information of the provider;
the characteristic value of the first dimension provider is calculated by the current information of the article provider and the information of all the article providers in the same category;
The second dimension provider characteristic value is calculated by the current information of the provider and the history information of the provider;
And calculating the first dimension provider characteristic value based on the provider characteristic value of the article provider and the statistical value of the provider characteristic value.
6. The method of claim 4, wherein the step of determining the position of the first electrode is performed,
And calculating the statistical value of the characteristic value of the provider of the article provider in the set time, and calculating the statistical value of the second dimension provider based on the characteristic value of the provider of the article provider and the statistical value of the characteristic value of the provider.
7. The method of claim 1, wherein the step of determining the position of the substrate comprises,
Acquiring an abnormal characteristic value of the article to be promoted by using an abnormal value detection model, and inputting the abnormal characteristic value into a convolutional neural network model to acquire a weight value of the article to be promoted; and calculating the promotion score of the article to be promoted according to the basic score of the article to be promoted, the grade value of the article provider and the weight value of the article to be promoted.
8. The method according to any one of claims 1 to 7, comprising:
receiving one or more item information, the item information comprising a promotion score; and determining the display position of the corresponding object on the page according to the promotion score.
9. An apparatus for generating item information, the apparatus for use in the method of generating item information of claim 1, comprising: the system comprises an article score calculating module, a popularization score calculating module and an article information generating module; wherein,
The article score calculating module is used for obtaining the category of the article to be promoted, calculating the basic score of the article to be promoted based on a back propagation algorithm model according to the characteristic data of the article contained in the category and the characteristic data of the article to be promoted; acquiring characteristics of an article provider of an article to be promoted, and calculating a grade value of the article provider based on a classification model according to the characteristics of the article contained in the category and the characteristics of the article provider;
The promotion score calculating module is used for determining the promotion score of the article to be promoted according to the basic score of the article to be promoted and the grade value of the article provider;
and the article information generation module is used for ordering and forming a corresponding article sequence to be promoted based on the promotion scores, acquiring a set number of articles from the article sequence to be promoted to generate corresponding article information, and sending the article information.
10. An apparatus for generating item information, the apparatus for use in the method of generating item information of claim 8, comprising: generating a page module; wherein,
The page generation module is used for receiving one or more item information, wherein the item information comprises a promotion score; and determining the display position of the corresponding object on the page according to the promotion score.
11. An electronic device, comprising:
One or more processors;
Storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-8.
12. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-8.
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