CN113313545B - Information recommendation method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to an information recommendation method, a device, a computer device and a storage medium, wherein when a shared object is leased, user portrait data are obtained according to user identification; and obtaining the identification of the rental cabinet; then searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information; predicting the commodity of interest of the user according to the user portrait data to obtain corresponding predicted popularization information; and if the preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to the user terminal. The application increases the function of the leasing system, and can pertinently recommend information to the user when the shared object is leased.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an information recommendation method, an information recommendation device, a computer device, and a storage medium.
Background
With the development of science and technology, although the use of mobile power supplies is more and more widespread, the mobile power supplies occupy a certain storage space and have a certain weight, and still have inconvenience for users, so that the shared mobile power supplies for providing mobile power lease services for users are generated.
In order to facilitate the renting of users, enterprises sharing the mobile power supply often put in products in a plurality of places. However, in the conventional technology, the single function of the shared mobile power source leasing system affects the usage rate of the shared mobile power source.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an information recommendation method, apparatus, computer device, and storage medium capable of diversifying the functions of a shared mobile power lease system.
An information recommendation method, the method comprising:
when the shared object is rented, acquiring user portrait data according to the user identifier;
acquiring a lease cabinet identification; the lease cabinet is used for providing the shared object for the user;
searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information;
Predicting the commodity of interest of the user according to the user portrait data to obtain corresponding predicted popularization information;
and if the preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to the user terminal.
In one embodiment, the acquiring the user portrait data according to the user identifier includes:
collecting user behavior data and user attribute data according to the user identification;
And generating the user portrait data according to the user behavior data and the user attribute data.
In one embodiment, the rental cabinet is associated with a sales counter; searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information, wherein the method comprises the following steps:
Searching according to the user portrait data to obtain preset popularization information comprising at least one of target sales counter and discount information matched with the user portrait data.
In one embodiment, the method further comprises:
acquiring crowd portrayal information of a user crowd corresponding to the preset popularization information;
and adjusting the preset popularization information according to the crowd image information.
In one embodiment, the generation method of the crowd image information includes:
Acquiring user portrait data of a plurality of users in the user group;
extracting keywords from the user portrait data to obtain feature labels of the users;
and clustering the characteristic labels of the users to obtain the crowd figure information.
In one embodiment, the method further comprises:
And responding to the lease instruction of the shared object, controlling the shared object to pop out of the lease cabinet, and simultaneously placing the commodity into the commodity taking port of the target sales counter.
In one embodiment, the searching according to the user portrait data and/or the rental cabinet identifier to obtain the corresponding preset popularization information includes:
acquiring preset popularization information of an operation area where the rental cabinet is located according to the identification of the rental cabinet;
and determining preset popularization information corresponding to the user portrait data in the preset popularization information of the operation area.
In one embodiment, the predicting the commodity of interest of the user according to the user portrait data to obtain the corresponding predicted popularization information includes:
inputting the user portrait data into a logistic regression model to obtain the recommendation probability of each commodity;
And determining the commodity with the recommendation probability meeting the preset condition as the commodity of interest of the user, and obtaining the predicted popularization information corresponding to the user portrait data.
In one embodiment, the location information corresponding to the predicted popularization information is store location information of the commodity of interest to the user; store position information of the commodity of interest to the user is determined by a correspondence relationship between commodity information and store position information.
In one embodiment, the generating method of the correspondence between the commodity information and the store location information includes:
acquiring commodity information and store position information of each commodity, wherein the store position information comprises identifiers of rental cabinet identifications;
and establishing a corresponding relation between commodity information and store position information according to the identifier of the leasing cabinet identifier, the commodity information and the store position information of each commodity.
An information recommendation apparatus, the apparatus comprising:
the portrait data acquisition module is used for acquiring user portrait data according to the user identification when the shared object is leased;
the rental cabinet identification acquisition module is used for acquiring the rental cabinet identification; the lease cabinet is used for providing the shared object for the user;
The preset information searching module is used for searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information;
the promotion information prediction module is used for predicting the commodity of interest of the user according to the user portrait data to obtain corresponding prediction promotion information;
And the position information recommending module is used for recommending the position information corresponding to the predicted popularization information to the user terminal if the preset popularization information is matched with the predicted popularization information.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
The information recommending method, the information recommending device, the computer equipment and the storage medium acquire user portrait data according to the user identification when the shared object is rented; and obtaining the identification of the rental cabinet; then searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information; predicting the commodity of interest of the user according to the user portrait data to obtain corresponding predicted popularization information; and if the preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to the user terminal. The application increases the function of the leasing system, and can pertinently recommend information to the user when the shared object is leased.
Drawings
FIG. 1 is an application environment diagram of an information recommendation method in one embodiment;
FIG. 2 is a flow chart of an information recommendation method according to an embodiment;
FIG. 3 is a flowchart illustrating step S210 in one embodiment;
FIG. 4 is a flowchart of another embodiment of an information recommendation method;
FIG. 5 is a flow diagram of a method of generating people group portrait information in one embodiment;
FIG. 6 is a schematic diagram of a rental cabinet versus a sales counter in one embodiment;
FIG. 7 is a flowchart of step S230 in one embodiment;
FIG. 8 is a flow chart of step S240 in one embodiment;
FIG. 9 is a flow chart of a method for generating a correspondence between merchandise information and store location information in one embodiment;
FIG. 10 is a block diagram schematically illustrating a structure of an information recommending apparatus in one embodiment;
FIG. 11 is a schematic diagram of the internal structure of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The information recommendation method provided by the application can be applied to an application environment shown in fig. 1. The system comprises a plurality of leasing cabinets 110, a server 120 and a terminal 130, wherein each leasing cabinet 110 is in communication connection with the server 120, and the server 120 is in communication connection with the terminal 130. Rental cabinet 110 is used to house a number of shared items to provide the shared items to a user. When the shared object in any one of the rental cabinets 110 is rented by the user, the user terminal sends the user identification to the server 120, and the rental cabinet 110 sends the rental cabinet identification to the server 120. The server 120 obtains user portrait data according to the user identifier, obtains the rental cabinet identifier, and searches according to the user portrait data and/or the rental cabinet identifier to obtain corresponding preset popularization information. The server 120 may be configured with a prediction model, so that the commodity of interest of the user can be predicted according to the user portrait data based on the prediction model, so as to obtain corresponding prediction popularization information; and if the preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to the user terminal.
The shared articles may be sharable articles such as shared umbrellas, shared rechargeable books and periodicals. The terminal 130 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 120 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, an information recommendation method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
S210, when the shared object is rented, user portrait data is acquired according to the user identification.
S220, acquiring the identification of the rental cabinet.
The leasing cabinet is used for providing the shared object for the user. When the user needs to use the shared object, the shared object is leased from the leasing cabinet, the leasing cabinet can send the leasing cabinet identification to the server, and the user terminal sends the user identification to the server. In some embodiments, the database of the server may have user portrait data stored therein in advance, and the server obtains the user portrait data from the database according to the user identifier after receiving the user identifier. In other embodiments, user profile data is stored in a computer device communicatively coupled to a server, and the server obtains the user profile data from the computer device based on a user identification.
And S230, searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information.
The preset promotion information is promotion information stored in the server in advance or in computer equipment in communication connection with the server. The preset popularization information can be commodity type information (men's clothing, tableware, restaurants, living goods, cosmetics, skin care products and the like), shop position information, commodity preferential information and the like, and can also be advertisement information, movie propaganda information and the like. The preset promotion information can also rent the geographic location or preferential information of the retail sales counter associated with the cabinet. Specifically, in one embodiment, the user portrait data may reflect the interests of the user or the needs of the user, so that corresponding preset promotion information is searched according to the user portrait data. In another embodiment, the rental cabinet is placed in a fixed geographic location, and the server stores a correspondence between the rental cabinet identification and the geographic location of the rental cabinet. Corresponding shops of various types or sales counter of various types are distributed around the geographic position of the rental cabinet, the corresponding relation between the identification of the rental cabinet and preset popularization information can be prestored in a server, and searching is carried out according to the identification of the rental cabinet to obtain the corresponding preset popularization information. In other embodiments, the server stores the corresponding relationship among the user portrait data, the rental cabinet identifier and the preset popularization information in advance, so that the corresponding preset popularization information can be obtained by searching according to the user portrait data and the rental cabinet identifier at the same time.
S240, predicting the commodity of interest of the user according to the user portrait data to obtain corresponding prediction popularization information.
The predicted popularization information is popularization information obtained by predicting commodities of interest to the user, for example, if the predicted popularization information predicts that the user is likely to be interested in the Su helper according to the user portrait data, the predicted popularization information can be a restaurant of 'Su helper'. For another example, if the user is predicted to be interested in early education based on the user portrait data, the predicted promotion information may be an early education institution of "XX baby". Specifically, a prediction model can be deployed at the server, user portrait data is input into the prediction model, a series of commodities are obtained to obtain the probability that the user is interested in the commodities, and commodities of interest to the user are determined from the series of commodities according to the probability of each commodity, so that corresponding prediction popularization information is obtained. It should be noted that, the prediction model may be implemented in a variety of ways, for example, a content-based recommendation algorithm, a collaborative filtering-based recommendation algorithm, an association rule-based recommendation algorithm, a utility-based recommendation algorithm, a knowledge-based recommendation algorithm, and so on. In some embodiments, collaborative filtering recommendation algorithm can be adopted, and a prediction model is established through a machine learning algorithm, so that the prediction of the commodity of interest to the user is realized. There are a variety of machine learning algorithms that apply collaborative filtering recommendations, such as Aspect Model, PLSA, LDA, clustering, SVD, matrix Factorization, LR, GBDT, and so on.
S250, if the preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to the user terminal.
Specifically, comparing preset popularization information with predicted popularization information, and recommending the position information corresponding to the predicted popularization information to a user terminal if the preset popularization information is matched with the predicted popularization information. For example, if the predicted popularization information is chips and the preset popularization information includes a brand of fast food, the chips are matched with the brand of fast food, and the position information of the brand of fast food is recommended to the user terminal so as to guide the user to go to the brand of fast food for consumption. If the predicted popularization information is a fried chicken, when the preset popularization information is a nail shop, the fried chicken may not be matched with the nail shop.
In the embodiment, when the shared object is rented, user portrait data is acquired according to the user identifier; and obtaining the identification of the rental cabinet; then searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information; predicting the commodity of interest of the user according to the user portrait data to obtain corresponding predicted popularization information; and if the preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to the user terminal. On the one hand, the function of the renting system is added, and when the shared object is rented, information recommendation can be performed to the user in a targeted manner. On the other hand, through the function of diversified lease systems, the enthusiasm of users for using the lease systems is improved, and therefore the utilization rate of shared articles is improved.
In one embodiment, as shown in fig. 3, in step S210, the obtaining user portrait data according to the user identifier includes:
s310, collecting user behavior data and user attribute data according to the user identification.
S320, generating the user portrait data according to the user behavior data and the user attribute data.
The user behavior data may include data related to user shopping (such as user shopping habit data, online shopping order data, offline transaction data, etc.), web page data browsed by the user, and the like, which are operation behavior data of the user at the user terminal. The user attribute data may be information such as the sex and age of the user, or data such as young, middle-aged, after 80, after 90, etc. The user attribute data may be user data filled in by the user when registering the application, or may be user attribute data obtained by predicting the user data. Specifically, user behavior data and user attribute data such as user shopping habits are collected according to user identifiers, the user attribute data is collected according to the user identifiers, and the user behavior data and the user attribute data are analyzed to obtain user portrait data. The user portrait data may reflect user preferences or user interests.
In this embodiment, the user behavior data and the user attribute data are collected according to the user identifier. And generating the user portrait data according to the user behavior data and the user attribute data. The user portrait data provides an accurate data basis for the subsequent prediction of the commodity of interest of the user, and the user retention rate can be improved through accurate information recommendation.
In one embodiment, the rental cabinet is associated with a sales counter; in step S230, the searching according to the user portrait data and/or the rental cabinet identifier to obtain corresponding preset popularization information includes: searching according to the user portrait data to obtain preset popularization information comprising at least one of target sales counter and discount information matched with the user portrait data.
In particular, rental bins are associated with sales outlets of a variety of types, such as sales outlets that sell snack foods, book boxes that sell books, beverage outlets, or paper towels outlets. A target sales counter that matches the customer representation data may be selected from the sales counter associated with the rental cabinet based on the customer representation data. The preferential discount information matched with the user portrait data can be searched according to the user portrait data, for example, if the user portrait data comprises 80 post-baby mothers, the preferential discount information of a certain brand of milk powder in the mother and infant store can be searched.
In this embodiment, the preset promotion information including at least one of the target sales counter and the discount information matched with the user portrait data is obtained by searching according to the user portrait data, so as to perform accurate information recommendation and navigation guidance to the user.
In one embodiment, as shown in fig. 4, the method further comprises:
s410, crowd portrait information of the user crowd corresponding to the preset popularization information is obtained.
S420, adjusting the preset popularization information according to the crowd image information.
Specifically, the user population includes a plurality of users, each user corresponding to a respective user profile. Crowd figure information can be obtained through user figure data of a plurality of users, and the server stores the corresponding relation between preset popularization information and user crowd and the corresponding relation between the user crowd and the crowd figure information. After the preset popularization information is determined, the corresponding user crowd can be determined according to the corresponding relation between the preset popularization information and the user crowd, so that crowd portrait information of the user crowd is obtained. The crowd image information is user data for the crowd of users, for example, the crowd image information can be after 80 or after 90, and can also be Buddha system, food, business, lipstick and the like. The crowd image information can reflect the overall characteristics of the crowd of users, and the users can induce the commonalities presented by the crowd, so that the crowd image information is utilized to adjust the preset popularization information. Illustratively, after the crowd image information includes 90, the recommendation may be adjusted based on the crowd image information after 90.
In this embodiment, crowd portrait information of the user crowd corresponding to the preset popularization information is obtained; and adjusting the preset popularization information according to the crowd image information. The accuracy of information recommendation is further improved, and more accurate guidance is provided for users.
In one embodiment, as shown in fig. 5, the generation mode of the crowd image information includes:
S510, acquiring user portrait data of a plurality of users in the user group.
S520, extracting keywords from the user portrait data to obtain the feature labels of the users.
And S530, clustering the feature labels of the users to obtain the crowd figure information.
Wherein the user portrayal data comprises behavior data, browsing data and attribute data. In some embodiments, there are a variety of clustering algorithms that can be employed, such as: K-Means clustering, mean shift clustering, density-based clustering, maximum Expectation (EM) clustering with Gaussian Mixture Model (GMM), condensed hierarchical clustering, and graph community detection clustering, etc. Specifically, aiming at a certain user group, a plurality of users are arranged in the user group, user portrait data of each user in the user group is obtained, keyword extraction is carried out on the user portrait data, and the feature labels of each user are obtained. And clustering the feature labels by using a clustering algorithm to obtain crowd portrayal information of the user crowd.
In some embodiments, K-Means clustering may be employed, the specific steps including: (1) First some classes/groups (i.e. a certain feature tag) are selected and their respective center points are randomly initialized. The center point is the same location as the vector length of each data point. This requires a priori knowledge of the number of classes (i.e. the number of center points). (2) The distance from each data point to the center point is calculated, and the closest to which center point the data point is classified into which class. (3) calculating the center point of each class as a new center point. (4) Repeating the steps (1) to (3) until the center of each class does not change much after each iteration. Alternatively, the center point may be initialized randomly a plurality of times, and then the one with the best operation result may be selected. Each category represents crowd portrayal information of a user crowd.
In this embodiment, crowd portrait information of a user crowd is obtained through a clustering algorithm, an accurate data basis is provided for adjusting preset popularization information, and information recommended to the user is ensured to include commodities or shops interested by the user.
In one embodiment, the method further comprises: and responding to the lease instruction of the shared object, controlling the shared object to pop out of the lease cabinet, and simultaneously placing the commodity into the commodity taking port of the target sales counter.
Specifically, as shown in FIG. 6, a rental cabinet 620 is associated with the sales counter 610. When a user triggers a lease instruction of the shared object, the server receives the lease instruction, and the server sends an instruction for ejecting the shared object to the lease cabinet to control the shared object to be ejected from the lease cabinet. Meanwhile, if the user selects to purchase the recommended interesting commodity, the server sends an instruction for throwing the interesting commodity to a target sales counter associated with the leasing cabinet, and the target sales counter throws the interesting commodity to a commodity taking port of the target sales counter. It should be noted that the rental cabinet 620 and the sales counter 610 may be structurally connected, for example, the sales counter 610 is located above or below the rental cabinet 620, and the sales counter 610 is located on the left side or the right side of the rental cabinet 620. The rental cabinet 620 and the sales counter 610 may be structurally separate, but both the rental cabinet 620 and the sales counter 610 may be remotely controlled by a server or terminal at the same time, such as completing the purchase of the merchandise at the same time when the shared merchandise is rented or completing the rental of the shared merchandise at the same time when the merchandise is purchased.
In the embodiment, the rental cabinet is associated with the sales counter, when the user rents the shared object, the shared object can be obtained from the rental cabinet, the object commodity of interest of the user can be obtained from the target sales counter, the function of the rental cabinet is expanded, meanwhile, the demand of the user for renting the shared object and shopping is met, and the operation cost of the user is reduced.
In one embodiment, as shown in fig. 7, in step S230, the searching according to the user portrait data and/or the rental cabinet identifier to obtain corresponding preset popularization information includes:
s710, acquiring preset popularization information of an operation area where the rental cabinet is located according to the identification of the rental cabinet.
S720, determining preset popularization information corresponding to the user portrait data in the preset popularization information of the operation area.
Specifically, the server stores the corresponding relation between the identification of the rental cabinet and the operation area where the rental cabinet is located, and the corresponding relation between the operation area and preset popularization information. And determining an operation area where the rental cabinet is located according to the identification of the rental cabinet, and acquiring preset popularization information such as shops, commodity offers and the like in the operation area according to the operation area where the rental cabinet is located. Further, the server stores a correspondence between the user portrait data and preset popularization information, so that the corresponding preset popularization information can be determined in the preset popularization information of the operation area according to the user portrait data.
In this embodiment, the preset popularization information of the operation area where the rental cabinet is located is obtained according to the identification of the rental cabinet; and determining preset popularization information corresponding to the user portrait data in the preset popularization information of the operation area, and recommending the preset popularization information near the rental cabinet to the user in a targeted manner, so that the conversion rate of the popularization information is improved.
In one embodiment, as shown in fig. 8, in step S240, the predicting, according to the user portrait data, the commodity of interest of the user to obtain corresponding predicted popularization information includes:
S810, inputting the user portrait data into a logistic regression model to obtain the recommendation probability of each commodity.
S820, determining the commodity with the recommendation probability meeting the preset condition as the commodity of interest of the user, and obtaining the predicted popularization information corresponding to the user portrait data.
In some embodiments, the logistic regression model may use LR (Logistic Regression) models, calculate the recommendation probability of each commodity through LR models, further rank all the commodities according to the recommendation probability, and select the commodity with the highest recommendation probability as the recommended commodity. It will be appreciated that it is also possible to select several items that are top ranked. Specifically, user portrait data is input into a logistic regression model, and the probability of each commodity purchased by the user is used as the recommendation probability of each commodity according to the user portrait data through the logistic regression model. The commodities can be ordered according to the recommendation probability of the commodities, and the preset number of commodities are used as commodities which are interested by a user according to the order from high to low, so that the corresponding prediction popularization information of the user portrait data is obtained. The probability threshold value can be preset, and if the recommendation probability of the commodity is larger than the probability threshold value, the commodity is the commodity of interest to the user. The commodity with the highest recommendation probability can also be determined to be the commodity of interest to the user.
In this embodiment, the user portrait data is input to a logistic regression model to obtain the recommendation probability of each commodity. And determining the commodity with the recommendation probability meeting the preset condition as the commodity of interest of the user, and obtaining the predicted popularization information corresponding to the user portrait data.
In one embodiment, the preset popularization information and the predicted popularization information are compared, and if the preset popularization information and the predicted popularization information are matched, the position information corresponding to the predicted popularization information is recommended to the user terminal. Specifically, the position information corresponding to the predicted popularization information is store position information of the commodity of interest to the user; store position information of the commodity of interest to the user is determined by a correspondence relationship between commodity information and store position information.
In one embodiment, as shown in fig. 9, the generation method of the correspondence between the commodity information and the store location information includes:
S910, acquiring information of each commodity and store position information of each commodity, wherein the store position information comprises an identifier of a rental cabinet identification.
S920, according to the identifier of the rental cabinet identification, the commodity information and the store position information of each commodity, establishing a corresponding relation between the commodity information and the store position information.
Specifically, commodity of interest to the user is obtained by predicting the user portrait data, so that a shop or store selling the commodity can be determined according to commodity information of the commodity of interest to the user, and store position information is determined as position information corresponding to predicted popularization information. The correspondence between commodity information and store position information is established in advance, and each commodity information and store position information of each commodity need to be acquired. The store location information may include an identifier of the rental cabinet identification (e.g., the rental cabinet identification may be a-21, the identifier may be a, and the identifier may be used to identify the operating area in which the rental cabinet is located). And establishing a corresponding relation between the commodity information and the store position information according to the identifier of the leasing cabinet identifier, the commodity information and the store position information of the commodities. Illustratively, the predicted promotional information may include a surrounding store type, a type of merchandise sold within the store. For example, the types of surrounding stores are classified into a top store, a zapa, an HM, and the like according to brands, or a gold jewelry store, a restaurant store, an apparel store, and the like according to commodity types. The types of merchandise sold in stores are classified into, for example, men's wear, tableware, umbrellas, watches, and the like. The commodity information is associated with the rental cabinet identification clothes in advance, so that preset popularization information can be obtained through the rental cabinet identification. For example, if the rental cabinets located at mall A are all identified as A, e.g., A-001, A-002, then the store or merchandise sold at mall A may be associated with prefix A, e.g., A-HM-men's clothing.
In one embodiment, the present application provides an information recommendation method, including:
s1010, collecting user behavior data and user attribute data according to the user identification when the shared object is leased.
S1020, generating the user portrait data according to the user behavior data and the user attribute data.
S1030, acquiring the identification of the rental cabinet.
Wherein, the leasing cabinet is associated with a sales counter; the lease cabinet is used for providing the shared object for the user;
S1040, searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information.
Specifically, searching is performed according to the user portrait data, and preset promotion information comprising at least one of target sales counter and preferential discount information matched with the user portrait data is obtained. Or alternatively
Acquiring preset popularization information of an operation area where the rental cabinet is located according to the identification of the rental cabinet; and determining preset popularization information corresponding to the user portrait data in the preset popularization information of the operation area.
S1050, obtaining crowd portrait information of the user crowd corresponding to the preset popularization information.
S1060, adjusting the preset popularization information according to the crowd image information.
And S1070, predicting the commodity of interest of the user according to the user portrait data to obtain corresponding prediction popularization information.
Specifically, inputting the user portrait data into a logistic regression model to obtain the recommendation probability of each commodity; and determining the commodity with the recommendation probability meeting the preset condition as the commodity of interest of the user, and obtaining the predicted popularization information corresponding to the user portrait data.
S1080, if the adjusted preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to the user terminal.
The position information corresponding to the predicted popularization information is shop position information of the commodity of interest to the user; store position information of the commodity of interest to the user is determined by a correspondence relationship between commodity information and store position information.
Further, the method for generating the correspondence between the commodity information and the store position information includes: acquiring commodity information and store position information of each commodity, wherein the store position information comprises identifiers of rental cabinet identifications; and establishing a corresponding relation between commodity information and store position information according to the identifier of the leasing cabinet identifier, the commodity information and the store position information of each commodity.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the steps or stages is not necessarily sequential, but may be performed in turn or alternately with at least a part of other steps or stages.
In one embodiment, as shown in fig. 10, there is provided an information recommendation apparatus including: the portrait data acquisition module 1010, the rental cabinet identification acquisition module 1020, the preset information search module 1030, the promotion information prediction module 1040, and the location information recommendation module 1050, wherein:
And the portrait data acquisition module 1010 is used for acquiring user portrait data according to the user identification when the shared object is leased.
A rental cabinet identification acquisition module 1020 for acquiring a rental cabinet identification; the rental cabinet is used for providing the shared object to the user.
And the preset information searching module 1030 is configured to search according to the user portrait data and/or the rental cabinet identifier, so as to obtain corresponding preset popularization information.
And the promotion information prediction module 1040 is used for predicting the commodity of interest of the user according to the user portrait data to obtain corresponding prediction promotion information.
The location information recommending module 1050 is configured to recommend location information corresponding to the predicted popularization information to a user terminal if the preset popularization information is matched with the predicted popularization information.
In one embodiment, the portrait data acquisition module 1010 is further configured to acquire user behavior data and user attribute data according to a user identifier; and generating the user portrait data according to the user behavior data and the user attribute data.
In one embodiment, the preset information searching module 1030 is further configured to search according to the user portrait data to obtain preset promotion information including at least one of a target sales counter and discount information matched with the user portrait data.
In one embodiment, the device further comprises a crowd information acquisition module and a promotion information adjustment module; wherein:
and the crowd information acquisition module is used for acquiring crowd portrait information of the user crowd corresponding to the preset popularization information.
And the promotion information adjustment module is used for adjusting the preset promotion information according to the crowd portrait information.
In one embodiment, the device further comprises a crowd information generating module, configured to obtain user portrait data of a plurality of users in the user crowd; extracting keywords from the user portrait data to obtain feature labels of the users; and clustering the characteristic labels of the users to obtain the crowd figure information.
In one embodiment, the apparatus further comprises a lease vending module for controlling the shared item to be ejected from the lease cabinet in response to a lease instruction of the shared item, and simultaneously placing the commodity into a pickup port position of the target vending cabinet.
In one embodiment, the preset information searching module 1030 is further configured to obtain preset popularization information of an operation area where the rental cabinet is located according to the identification of the rental cabinet; and determining preset popularization information corresponding to the user portrait data in the preset popularization information of the operation area.
In one embodiment, the promotion information prediction module 1040 is further configured to input the user portrait data into a logistic regression model, so as to obtain a recommendation probability of each commodity; and determining the commodity with the recommendation probability meeting the preset condition as the commodity of interest of the user, and obtaining the predicted popularization information corresponding to the user portrait data.
In one embodiment, the location information corresponding to the predicted popularization information is store location information of the commodity of interest to the user; store position information of the commodity of interest to the user is determined by a correspondence relationship between commodity information and store position information.
In one embodiment, the apparatus further comprises a correspondence generation module, configured to obtain information of each commodity and store location information of each commodity, where the store location information includes an identifier of a rental cabinet identifier; and establishing a corresponding relation between commodity information and store position information according to the identifier of the leasing cabinet identifier, the commodity information and the store position information of each commodity.
For specific limitations of the information recommendation device, reference may be made to the above limitation of the information recommendation method, and the description thereof will not be repeated here. The respective modules in the information recommendation apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 11. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement an information recommendation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 11 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor executing the method steps of any of the embodiments described above.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the method steps of any of the embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. An information recommendation method, the method comprising:
when the shared object is rented, acquiring user portrait data according to the user identifier;
acquiring a lease cabinet identification; the lease cabinet is used for providing the shared object for the user;
Searching according to the user portrait data and the rental cabinet identification to obtain corresponding preset popularization information, wherein the preset popularization information comprises popularization information stored in a server in advance or computer equipment in communication connection with the server, and the server stores the corresponding relation among the user portrait data, the rental cabinet identification and the preset popularization information in advance;
Predicting the commodity of interest of the user according to the user portrait data to obtain corresponding predicted popularization information;
If the preset popularization information is matched with the predicted popularization information, recommending the position information corresponding to the predicted popularization information to a user terminal, wherein the position information corresponding to the predicted popularization information is shop position information of the commodity of interest to the user; store position information of the commodity of interest to the user is determined through a corresponding relation between commodity information and store position information;
searching according to the user portrait data and the rental cabinet identification to obtain corresponding preset popularization information, wherein the method comprises the following steps:
acquiring preset popularization information of an operation area where the rental cabinet is located according to the identification of the rental cabinet;
Determining preset popularization information corresponding to the user portrait data in the preset popularization information of the operation area;
The method for predicting the commodity of interest of the user according to the user portrait data to obtain corresponding predicted popularization information comprises the following steps:
inputting the user portrait data into a logistic regression model to obtain the recommendation probability of each commodity;
And determining the commodity with the recommendation probability meeting the preset condition as the commodity of interest of the user, and obtaining the predicted popularization information corresponding to the user portrait data.
2. The method of claim 1, wherein the obtaining user portrait data according to a user identifier includes:
collecting user behavior data and user attribute data according to the user identification;
And generating the user portrait data according to the user behavior data and the user attribute data.
3. The method of claim 2, wherein the rental cabinet is associated with a sales counter; searching according to the user portrait data and/or the rental cabinet identification to obtain corresponding preset popularization information, wherein the method comprises the following steps:
Searching according to the user portrait data to obtain preset popularization information comprising at least one of target sales counter and discount information matched with the user portrait data.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring crowd portrayal information of a user crowd corresponding to the preset popularization information;
and adjusting the preset popularization information according to the crowd image information.
5. The method of claim 4, wherein the generation of the crowd image information comprises:
Acquiring user portrait data of a plurality of users in the user group;
extracting keywords from the user portrait data to obtain feature labels of the users;
and clustering the characteristic labels of the users to obtain the crowd figure information.
6. A method according to claim 3, characterized in that the method further comprises:
And responding to the lease instruction of the shared object, controlling the shared object to pop out of the lease cabinet, and simultaneously placing the commodity into the commodity taking port of the target sales counter.
7. The method according to claim 1, wherein the generation method of the correspondence relationship between the commodity information and the store position information includes:
acquiring commodity information and store position information of each commodity, wherein the store position information comprises identifiers of rental cabinet identifications;
and establishing a corresponding relation between commodity information and store position information according to the identifier of the leasing cabinet identifier, the commodity information and the store position information of each commodity.
8. An information recommendation device, characterized in that the device comprises:
the portrait data acquisition module is used for acquiring user portrait data according to the user identification when the shared object is leased;
the rental cabinet identification acquisition module is used for acquiring the rental cabinet identification; the lease cabinet is used for providing the shared object for the user;
The preset information searching module is used for searching according to the user portrait data and the rental cabinet identification to obtain corresponding preset popularization information, wherein the preset popularization information comprises popularization information stored in a server in advance or in computer equipment in communication connection with the server, and the server is pre-stored with the corresponding relation among the user portrait data, the rental cabinet identification and the preset popularization information; the method is also used for acquiring preset popularization information of an operation area where the rental cabinet is located according to the identification of the rental cabinet; determining preset popularization information corresponding to the user portrait data in the preset popularization information of the operation area;
The promotion information prediction module is used for predicting the commodity of interest of the user according to the user portrait data to obtain corresponding prediction promotion information; the user portrait data is also used for inputting the user portrait data into a logistic regression model to obtain the recommendation probability of each commodity; determining commodities with recommendation probability meeting preset conditions as commodities of interest of the user, and obtaining forecast promotion information corresponding to the user portrait data;
the position information recommending module is used for recommending the position information corresponding to the predicted popularization information to a user terminal if the preset popularization information is matched with the predicted popularization information, wherein the position information corresponding to the predicted popularization information is shop position information of the commodity of interest to the user; store position information of the commodity of interest to the user is determined by a correspondence relationship between commodity information and store position information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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