TW202209220A - Recommended target user selecting method, system, equipment and storage medium - Google Patents

Abstract

A recommended target user selecting method, system, equipment and storage medium. The method comprises: obtaining an item information to be recommended; obtaining an associated user of a recommender from a social platform, and using the associated user as a candidate target user for an item recommendation, and obtaining a user information of the candidate target user; selecting a recommended target user of the item to be recommended from the candidate target user, according to the item information to be recommended and the user information of the candidate target user; pushing the recommended target user information to a user terminal of the recommender. The present invention, when recommending the items based on social platforms, selects a recommendee having a high probability of accepting items, improves the recommendation conversion rate, and avoids the recommendee from receiving too much useless information.

Description

Selection method, system, device and storage medium for recommending target users

The present invention relates to the technical field of data processing, and in particular, to a method, system, device and storage medium for selecting a recommended target user.

In order to increase shipments, merchants are usually willing to offer more favorable prices to consumers with large purchases to stimulate purchases. Existing consumers’ group purchases are mostly limited by regions. Often neighbors in the same community or colleagues from the same company make group purchases to obtain group purchase prices. With the development of the Internet and logistics, some shopping platforms can also provide online shopping malls. The function of group and single, you can join the group purchase directly from the Internet, and can be distributed to different regions, breaking the geographical restrictions.

For example, in the existing technology, in order to successfully increase the shipment volume, and also help consumers to successfully obtain group purchase prices and increase their dependence on the platform, the shopping platform integrates "e-commerce" and "social networking", so that users can The selection of recommended target users is sent to family members and friends through social platforms to join groups and purchase goods at preferential prices.

However, if the general user does not ask individually, it is impossible to know whether family members and friends need the recommended products. If the recommendation information is widely distributed in order to obtain the group purchase price, most of the recommenders will receive information about the unwanted products. And too many unhelpful recommendations may cause trouble for friends, and even affect the referee's evaluation of the recommender.

In view of the problems in the prior art, the purpose of the present invention is to provide a selection method, system, device and storage medium for recommending target users. When recommending items based on social platforms, the recommenders who have a high probability of accepting the items are selected, and the recommendation conversion is improved. rate, and avoid the referee from receiving too much useless information.

In some embodiments, a method for selecting a recommended target user includes the following steps:

Get the item information to be recommended;

Obtain the recommender's associated users from the social platform, use the associated user as the candidate target user for item recommendation, and obtain the user information of the candidate target user;

According to the item information to be recommended and the user information of the candidate target user, the recommended target user of the item to be recommended is selected from the candidate target users;

Push the recommendation target user information to the user terminal of the recommender.

In some embodiments, determining the recommended target user of the item to be recommended according to the item information to be recommended and the user information of the candidate target user includes the following steps:

Obtain the historical behavior logs of each candidate target user on the shopping platform;

Whether the candidate target user is the recommended target user of the item to be recommended is determined according to the historical behavior log of the candidate target user.

In some embodiments, determining whether the candidate target user is the recommended target user of the item to be recommended according to the historical behavior log of the candidate target user includes the following steps:

Determine the related items of the candidate target user according to the historical behavior log of the candidate target user;

Determine whether the item to be recommended is a related item of the candidate target user;

If yes, the candidate target user is the recommended target user of the item to be recommended.

In some embodiments, determining whether the candidate target user is the recommended target user of the item to be recommended according to the historical behavior log of the candidate target user includes the following steps:

Determine the related items of the candidate target user according to the historical behavior log of the candidate target user;

Query similar items of related items in the item model according to the related items, and the item model is configured to store the similar items of multiple items;

Determine whether the item to be recommended is a related item of the candidate target user or a similar item of the related item;

If yes, the candidate target user is the recommended target user of the item to be recommended.

In some embodiments, after acquiring the historical behavior log of the candidate target user on the shopping platform, the following steps are further included:

If the historical behavior log of the candidate target user is not obtained, or the historical behavior log of the candidate target user does not meet the preset log analysis conditions, the similar users of the candidate target user are queried according to the user model, and the user model is configured to store multiple Similar users of each user;

Query related items of similar users of alternative target users;

Determine whether the item to be recommended is a related item of a similar user of the candidate target user;

If yes, the candidate target user is the recommended target user of the item to be recommended.

In some embodiments, determining the related items of the candidate target user according to the historical behavior log of the candidate target user includes the following steps:

When the candidate target user is a group, obtain the historical behavior logs of all single users in the group;

According to the historical behavior logs of all single users in the group, the related items of the group are determined as the related items of the group.

In some embodiments, the method for selecting a recommended target user further includes the following steps:

After sending the item recommendation information to the recommendation target user, obtain the feedback data of the recommendation target user;

Adjust the related items corresponding to the recommended target users according to the feedback data.

In some embodiments, a selection system for recommending target users is used to implement a method for recommending target users, wherein the selection system for recommending target users includes:

The item information collection module is used to obtain the item information to be recommended;

The candidate user acquisition module is used to acquire the associated users of the recommender from the social platform, use the associated users as the candidate target users for item recommendation, and obtain the user information of the candidate target users;

The target user selection module is used to select the recommended target user of the item to be recommended from the candidate target users according to the item information to be recommended and the user information of the candidate target user;

The target user push module is used to push the recommended target user information to the user terminal of the recommender.

In some embodiments, the selection device of the recommended target user includes:

processor;

storage in which executable instructions for the processor are stored;

Wherein, the processor is configured to perform the steps of the method for selecting the recommended target user by executing the executable instructions.

In some embodiments, the computer can read the storage medium for storing the program, and when the program is executed, the steps of the method for selecting the recommended target user are implemented.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

The method, system, device and storage medium for selecting a recommended target user of the present invention have the following beneficial effects:

When recommending an item based on a social platform, the present invention selects the target user recommended by the item from the recommender's friends on the social platform through the correlation between the user and the item, so that the recommender with a high probability of accepting the item can be selected, which greatly reduces the The chance of recommending the wrong object increases the conversion rate of recommendation. For the recommender, the recommended information received is likely to be of interest to him, so as to avoid the recommender from receiving too much useless information and improve the user experience. , and also avoid the waste of item recommendation traffic.

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments, however, can be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same drawing symbols in the drawings denote the same or similar parts, and thus their repeated descriptions will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and/or processor devices and/or microcontroller devices these functional entities.

FIG. 1 is a flowchart of a method for selecting a recommended target user according to some embodiments of the present application. As shown in FIG. 1, in some embodiments, a method for selecting a recommended target user includes the following steps:

Step S100: obtaining the item information to be recommended;

Step S200: Obtain the associated user of the recommender from the social platform, use the associated user as the candidate target user for item recommendation, and obtain user information of the candidate target user;

Step S300: According to the item information to be recommended and the user information of the candidate target user, select the recommended target user of the item to be recommended from the candidate target users;

Step S400: Push the recommendation target user information to the user terminal of the recommender.

In some embodiments, in the method for selecting a recommended target user, first obtain the item information to be recommended and the user information of the candidate target user through steps S100 and S200. Among them, in step S100, the item is a generalized concept, which can be a commodity to be recommended. For example, the commodity selected by the user on the shopping platform wants to be recommended to other friends, or it can also be an activity to be recommended, a to-be-recommended commodity Videos, articles to be recommended, etc. In step S200, the candidate target user is determined from the associated users of the recommender in the social platform. Here, the social platform may include social networking platforms such as WeChat and Alipay, and the associated users of the recommender may include the recommender's friends on the social platform and the groups he joins, and so on.

Further, in some embodiments, when an item is recommended based on a social platform through step S300, a target user for the item recommendation is selected from the recommender's social platform friends through the correlation between the user and the item. After the target user is selected, the target user information is pushed to the user terminal of the recommender through step S400, and the recommender can directly operate the user terminal to recommend the item to the target user on the user terminal. The user terminal here refers to the terminal equipment used by the user, including but not limited to mobile phones, tablet computers, notebook computers, etc. The target user information here may be identification information such as the ID of the target user. For example, when the recommender selects the recommended item, the user terminal of the recommender jumps to the APP interface of the social platform selected by the user for the recommender to select the recommended object. The information of the target user is pushed to the user terminal, and after receiving the information of the target user, the user terminal displays a recommended object selection interface.

FIG. 2 is an interface diagram of the recommended object selection interface J100 according to some embodiments of the present application. As shown in FIG. 2 , in some embodiments, FIG. 2 is a schematic diagram of a recommended object selection interface J100 displayed in a user terminal. The friend selection area of the recommendation object selection interface J100 can display the nickname or remark name of each target user, and the recommender can directly select the recommendation object in this interface and send the recommendation information. Specifically, the friend selection area can be divided into two parts: a selection area J110 for recommending target users and a selection area J120 for non-recommended target users. Displayed in the selection area J110 are the recommended target users selected through steps S100 to S300, and other friends of the recommender can be listed in the selection area J120. The two selection areas J110 and J120 can be identified in different ways, such as using different background colors, using different selection icons, using different text formats, and so on.

Therefore, the present invention can select recommenders who have a high probability of accepting items, greatly reduce the chance of recommending wrong objects, and improve the recommendation conversion rate. For the recommenders, the received recommendation information is more likely to be of interest to them. , so that the recommender can avoid receiving too much useless information. The present invention makes full use of the social network on the basis of recommending advertisements according to the characteristics of individual users by linking the social platform with the shopping platform (in other scenarios, it can be a film and television platform, an activity platform, an e-book platform, etc.). The advantages of , realize targeted mutual recommendation between users, and in the case of improving the conversion rate of advertising traffic, it is also more convenient for users to learn more about the products they are interested in.

FIG. 3 is a flowchart of selecting and recommending target users according to some embodiments of the present application. As shown in FIG. 3, in some embodiments, in step S300, according to the item information to be recommended and the user information of the candidate target user, determining the recommended target user of the item to be recommended includes the following steps:

Step S310: Obtain the historical behavior logs of each candidate target user on the shopping platform;

Step S320: Determine whether the candidate target user is the recommended target user of the item to be recommended according to the historical behavior log of the candidate target user.

In some embodiments, step S320: determining whether the candidate target user is the recommended target user of the item to be recommended according to the historical behavior log of the candidate target user, including the following steps:

Determine the related items of the candidate target user according to the historical behavior log of the candidate target user;

Determine whether the item to be recommended is a related item of the candidate target user;

If yes, the candidate target user is the recommended target user of the item to be recommended.

Therefore, in some embodiments, the present invention can analyze the related items of the target user in combination with the historical behavior log of each user on the shopping platform, and select the recommended target user according to the related items, so that the appropriate user can be selected according to the historical operation behavior of the user. recommended target users to improve the pertinence of target user selection.

In some embodiments, considering the limited number of users' historical behavior logs, only relying on related items to select target users has great limitations. Therefore, it is also possible to construct an item model according to the degree of association between items, further expand the scope of related items based on the item model, and add similar items to the related items to select candidate target users.

Step S320: Determine whether the candidate target user is the recommended target user of the item to be recommended according to the historical behavior log of the candidate target user, including the following steps:

Step S321: Determine the related items of the candidate target user according to the historical behavior log of the candidate target user;

Here, the related items of the candidate target user are determined, which can be selected according to the items that the user has browsed, clicked or purchased in the historical behavior log, and the user's score for each item can be calculated according to the user's different operation types , for example, the rating of an item that the user has browsed and the browsing time is greater than a certain threshold is "a", the rating of an item that the user has clicked is "b", etc. If the user has rejected the recommendation of an item, the user will The score minus a certain score, and finally select the item with a score higher than a certain score threshold as the user's associated item;

Step S322: Querying similar items of the associated item in the item model according to the associated item, and the item model is configured to store similar items of multiple items, that is, to store similar items of each item;

Step S323: judging whether the item to be recommended is a related item of the candidate target user or a similar item of the related item;

If yes, continue to step S324: the candidate target user is the recommended target user of the item to be recommended;

If not, proceed to step S325: the candidate target user is not the recommendation target user of the item to be recommended.

In some embodiments, item models may be constructed based on the degree of similarity between items. For each item, a feature vector of the item is constructed by relying on the attribute values of various attributes of the item, and the attributes of the item may include name, commodity category, applicable group, and group characteristics for searching the item. Then, the similarity can be calculated according to the feature vector of the two items, and the similarity can be calculated by cosine similarity, Euclidean distance, etc. After calculating the similarity "sim(j, i)", an item matrix can be constructed. For n items, the item matrix includes an array of similarity values of the i-th item and the j-th item. In addition, the similarity of two items can be calculated by further combining the user's search relevance. For example, multiple users will search for "Item B" at the same time when searching for "Item A", then it can be considered that "Item A" and "Item B" Item" is a similar item. The finally obtained item model may include a collection of similar items "N(u)" of each item, and the similarity between the similar item and the item is greater than a preset similarity threshold.

In some embodiments, for example, for a sphygmomanometer, a collection of similar items "N(u)" can be found including blood glucose meters, electronic sphygmomanometers, elderly health care, electronic product agents, smart electronic sphygmomanometers, and so on. After a user has searched for a blood pressure meter, if his social platform friends have a recommendation of a blood glucose meter, the user who has searched for a blood pressure meter can also be used as a recommended target user of the blood glucose meter.

In some embodiments, if a user has a small number of historical behavior logs in the shopping platform, the user model can be further combined with the constructed user model and the degree of correlation between users to select the recommended target user.

Specifically, step S310: after obtaining the historical behavior logs of each candidate target user on the shopping platform, if the obtained historical behavior logs meet the preset log analysis conditions, proceed to step S320, where the preset log analysis conditions can be is a predetermined number of logs. If the number of logs is too small, the preset log analysis conditions are not met. If the number of logs reaches the predetermined number of logs, step S320 may be continued.

If the historical behavior log of the candidate target user is not obtained, or the historical behavior log of the candidate target user does not meet the preset log analysis conditions, continue with the following steps:

Step S331: query the similar users of the candidate target user according to the user model, and the user model is configured to store the similar users of multiple users, that is, to store the similar users of each user;

Step S332: query the related items of similar users of the candidate target user, and further expand the scope of related items of similar users according to the item model, and use similar items of related items of similar users as related items of similar users;

Step S333: judging whether the item to be recommended is a related item of a similar user of the candidate target user;

If yes, continue to step S324: the candidate target user is the recommended target user of the item to be recommended;

If not, proceed to step S325: the candidate target user is not the recommendation target user of the item to be recommended.

Among them, the user model may be to collect the feature vector of each user in advance, and find the similar user collection "N(u)" of each user according to the similarity of the feature vectors between the two users. The similarity between the similar user and the user is greater than the preset similarity threshold. Specifically, the similarity between every two users may adopt cosine similarity, Euclidean distance, or the like. A user matrix can be constructed according to the similarity between users, and the user matrix includes the similarity between the ith user and the jth user. The user's feature vector can be composed of attribute values of multiple attributes of the user. The user's attributes can include basic attributes, such as the user's age, gender, region, etc., and can also include attributes analyzed according to the user's behavior habits, such as including User's shopping frequency, user's consumption habits, etc.

Here, in some embodiments, a method for selecting a recommended target user is specifically described with an example of recommending a sphygmomanometer. When a "user A" wants to recommend a sphygmomanometer to his friends, he first obtains the friend behavior logs of "friend 1, 2, 3..." of "user A", and then judges "friend 1, 2, 3..." ..." Have you browsed or clicked on items related to electrocardiogram, blood glucose meter, blood pressure monitor, etc. during the operation of the shopping platform? If yes, if "Friend 1" has browsed the blood glucose meter, the blood glucose meter can be determined according to the item model. If it is a similar item of a sphygmomanometer, then "Friend 1" is interested in the high probability of a sphygmomanometer, and "Friend 1" is selected as the recommended target user.

If "Friend 2" has no historical behavior log, or there is very little data in the historical behavior log, search for similar users of "Friend 2" according to the user model, and obtain "Similar User B", "Similar User C", etc. If "Similar User The related item of "B" is a sphygmomanometer, then it can be determined that "Friend 2" is interested in the high probability of the sphygmomanometer, and "Friend 2" is selected as the recommended target user.

Therefore, in some embodiments, friends can be divided into items to be recommended that have a high probability of interest or a low probability of being interested according to the user model and the item model. When a user needs to recommend an item, he can preferentially select a recommendation with a high probability of being interested. For target users, avoid interfering with uninterested friends, and reduce the possibility of users receiving meaningless and uninteresting recommendations. For the recommenders, they can receive products or activities related to their needs or preferences. Shopping platforms can offer preferential prices to attract consumers to recommend products to others. In order to attract more participants and film and television platforms to attract more viewers, they are more willing to provide discounts/bonuses/points to attract existing users to assist in promotion. . When recommending items, recommenders can accurately find social friends with similar preferences, and can accurately recommend products, activities, etc., to achieve the purpose of group purchase, so that both the recommender and the referee can benefit.

In some embodiments, in step S321, the related items of the candidate target user are determined according to the historical behavior log of the candidate target user, including the following steps:

When the candidate target user is a group, obtain the historical behavior logs of all single users in the group, where the group includes multiple single users, that is, obtain the historical behavior logs of all multiple single users in the group;

According to the historical behavior logs of all single users in the group, the related items of the group are determined as the related items of the group. Specifically, that is to find the overlapping related items of all single-user users in the group as the related items of the group. For example, if basically all users in a group have browsed fitness products, the fitness items can be used as related items of the group.

In some embodiments, the method for selecting a recommended target user further includes the following steps:

After sending the item recommendation information to the recommended target user, obtain feedback data of the recommended target user, the feedback data may include the user's operations such as browsing, clicking, and purchasing of the recommended item, and may also include the user's rejection of the recommended item;

Adjust the related items corresponding to the recommended target user according to the feedback data, that is, according to the user's rejection history, the item will be downgraded, and the item will be added to the user's related items according to the user's browsing, clicking, purchasing and other operations on the recommended item.

FIG. 4 is a schematic structural diagram of a system 100 for recommending target users according to some embodiments of the present application. As shown in FIG. 4 , in some embodiments, a system 100 for selecting a recommended target user is used to implement the method for selecting a recommended target user, and the system 100 for selecting a recommended target user includes:

The item information collection module M100 is used to obtain the item information to be recommended;

The candidate user acquisition module M200 is used to acquire the associated user of the recommender from the social platform, take the associated user as the candidate target user of the item recommendation, and acquire the user information of the candidate target user;

The target user selection module M300 is used to select the recommended target user of the item to be recommended from the candidate target users according to the item information to be recommended and the user information of the candidate target user;

The target user push module M400 is used to push the recommended target user information to the user terminal of the recommender.

In some embodiments, the selection system 100 for recommending target users first obtains the item information to be recommended and the candidate target user information for selection through the item information collection module M100 and the candidate user acquisition module M200, respectively, and the target user selects the module The group M300 selects the target user recommended by the item from the recommender's social platform friends through the correlation between the user and the item, and pushes the target user information to the recommender's user terminal through the target user push module M400, thereby You can choose recommenders who have a high probability of accepting items, greatly reduce the chance of recommending wrong objects, and improve the recommendation conversion rate. For the recommenders, the recommended information they receive is likely to be of interest to them, so it can be avoided. The recommender receives too much useless information, which improves the user experience and avoids the waste of item recommendation traffic.

In some embodiments, in the system 100 for selecting a recommended target user, the functions of each module can be implemented by using the specific implementation manner of the above-mentioned method for selecting a recommended target user, which will not be repeated here.

In some embodiments, the recommendation target user's selection device includes a processor and storage. The storage stores executable instructions of the processor. Wherein, the processor is configured to perform the steps of the method for selecting the recommended target user by executing the executable instructions. In some embodiments, the storage is also referred to as a storage device.

As will be appreciated by one skilled in the art, various aspects of the present invention may be implemented as a system, method or program product. Accordingly, various aspects of the present invention may be embodied in the following forms: an entirely hardware implementation, an entirely software implementation (including firmware, microcode, etc.), or an implementation in which both hardware and software aspects are combined. Collectively referred to as the "Circuit", "Module" or "Platform".

FIG. 5 is a schematic structural diagram of a selection device for recommending target users according to some embodiments of the present application. The electronic device 600 according to this embodiment of the present invention is described below with reference to FIG. 5 . The electronic device 600 shown in FIG. 5 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present invention.

In some embodiments, as shown in FIG. 5, electronic device 600 takes the form of a general-purpose computing device. Components of the electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus bar 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.

The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the above section of the method for selecting a recommended target user. For example, the processing unit 610 may perform the steps shown in FIG. 1 .

In some embodiments, the storage unit 620 is also referred to as a memory, and the storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and/or cache memory 6202, and may also A read only memory (ROM) 6203 is further included.

The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205 including, but not limited to, an operating system, one or more application programs, other program modules, and Program data, each or some combination of these examples may include an implementation of a network environment.

Bus 630 may be representative of one or more of several types of bus structures, including memory cell buses or memory cell controllers, peripheral buses, graphics acceleration ports, processing units, or using any of a variety of bus structures area bus.

The electronic device 600 may also communicate with one or more external devices 700 (eg, keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 650 . Furthermore, the electronic device 600 can also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network, such as Internet) communications. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus bar 630 . It should be appreciated that, although not shown, other hardware and/or software modules may be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems , tape drives, and data backup storage systems.

In some embodiments, the computer can read the storage medium for storing the program, and when the program is executed, the steps of the method for selecting the recommended target user are implemented. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes program code, when the program product is executed on a terminal device, the program code is used to make the terminal device execute the above recommendations in this specification. The steps according to various exemplary embodiments of the present invention are described in the section on the selection method of the target user.

FIG. 6 is a schematic structural diagram of a computer-readable storage medium according to some embodiments of the present application. Referring to FIG. 6, in some embodiments, FIG. 6 depicts a program product 800 for implementing the above method according to an embodiment of the present invention, which may employ a compact disk-read only memory (CD-ROM) and include program code, and It can be executed on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

Program product 800 may employ any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable hard drives, hard drives, random access memory (RAM), read only memory ( ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, compact disk-read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

A computer-readable storage medium may include a data signal in baseband or propagated as part of a carrier wave, carrying readable program code therein. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable storage medium can also be any readable medium other than a readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages—such as “Java”, “C++”, etc., as well as conventional procedural Programming language - such as the "C" language or similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on. In the case of a remote computing device, the remote computing device may be connected to the user computing device over any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, use an internet service provider to connect via the internet).

To sum up, by adopting the method, system, device and storage medium for recommending target users of the present invention, when recommending an item based on a social platform, through the correlation between the user and the item, from the recommender's social platform friends Select the target user for the item recommendation, so that you can choose the recommender who has a high probability of accepting the item, greatly reducing the chance of recommending the wrong object, and improving the recommendation conversion rate. For the recommender, the recommended information received is most likely to be yourself It can prevent the recommender from receiving too much useless information, improve the user experience, and avoid the waste of item recommendation traffic.

The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be considered that the specific implementation of the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the present invention, some simple deductions or substitutions can be made, which should be regarded as belonging to the protection scope of the present invention.

S100~S400: Steps J100: Recommended Object Selection Interface J110: Selection area J120: Selection area S310, S320, S321~S325, S331~S333: Steps 100: A selection system for recommending target users M100: Item Information Collection Module M200: Alternative User Acquisition Module M300: The target user selects the module M400: target user push module 600: Electronic Equipment 610: Processing unit 620: Storage Unit 6201: Random Access Memory 6202: Cache memory 6203: read-only memory 6204: Programs/Utilities 6205: Program Module 630: Busbar 640: Display unit 650: input/output interface 660: Network Adapter 700: External device 800: Program Products

FIG. 1 is a flowchart of a method for selecting a recommended target user according to some embodiments of the present application. FIG. 2 is an interface schematic diagram of a recommended object selection interface according to some embodiments of the present application. FIG. 3 is a flowchart of selecting and recommending target users according to some embodiments of the present application. FIG. 4 is a schematic structural diagram of a system for selecting a recommended target user according to some embodiments of the present application. FIG. 5 is a schematic structural diagram of a selection device for recommending target users according to some embodiments of the present application. FIG. 6 is a schematic structural diagram of a computer-readable storage medium according to some embodiments of the present application.

S100~S400: Steps

Claims (10)

A method for selecting a recommended target user, comprising the following steps: Get information about an item to be recommended; Acquire an associated user of a recommender from a social platform, use the associated user as a candidate target user for an item recommendation, and acquire user information of the candidate target user; According to the item information to be recommended and the user information of the candidate target user, a recommendation target user of an item to be recommended is selected from the candidate target users; and Pushing a recommendation target user information to a user terminal of the recommender. The method for selecting a recommended target user according to claim 1, wherein determining the recommended target user of the item to be recommended according to the item information to be recommended and the user information of the candidate target user includes the following steps: Obtain a historical behavior log of the candidate target user on a shopping platform; and Whether the candidate target user is the recommendation target user of the item to be recommended is determined according to the historical behavior log of the candidate target user. The method for selecting a recommended target user according to claim 2, wherein determining whether the candidate target user is the recommended target user of the item to be recommended according to the historical behavior log of the candidate target user includes the following steps: Determine an associated item of the candidate target user according to the historical behavior log of the candidate target user; determine whether the item to be recommended is the related item of the candidate target user; and If yes, the candidate target user is the recommended target user of the item to be recommended. The method for selecting a recommended target user according to claim 2, wherein determining whether the candidate target user is the recommended target user of the item to be recommended according to the historical behavior log of the candidate target user includes the following steps: Determine an associated item of the candidate target user according to the historical behavior log of the candidate target user; Query a similar item of the associated item in an item model according to the associated item, the item model is configured to store the similar item of each of a plurality of items; Determine whether the item to be recommended is the related item of the candidate target user or the similar item of the related item; and If yes, the candidate target user is the recommended target user of the item to be recommended. The method for selecting a recommended target user according to claim 2, wherein after acquiring the historical behavior log of the candidate target user on the shopping platform, the method further includes the following steps: If the historical behavior log of the candidate target user is not obtained, or the historical behavior log of the candidate target user does not meet a preset log analysis condition, query a similarity of the candidate target user according to a user model a user, the user model is configured to store a similar user of each of the plurality of users; query a related item of the similar user of the candidate target user; determine whether the item to be recommended is the related item of the similar user of the candidate target user; and If yes, the candidate target user is the recommended target user of the item to be recommended. The method for selecting a recommended target user according to request item 3 or 4, wherein determining the related item of the candidate target user according to the historical behavior log of the candidate target user, comprising the following steps: When the candidate target user is a group, obtain a historical behavior log of all single users in the group; and According to the historical behavior logs of all the single users in the group, a related item of the group is determined as the related item of the group. The method for selecting a recommended target user according to claim 3 or 4, wherein the method for selecting a recommended target user further comprises the following steps: After sending item recommendation information to the recommendation target user, obtain feedback data of the recommendation target user; and A related item corresponding to the recommended target user is adjusted according to the feedback data. A system for selecting a recommended target user, for implementing the method for selecting a recommended target user according to any one of request items 1 to 7, wherein the system for selecting a recommended target user includes: an item information collection module for acquiring information about an item to be recommended; an alternative user acquisition module, configured to acquire an associated user of a recommender from a social platform, use the associated user as an alternative target user for an item recommendation, and obtain a user information of the alternative target user; a target user selection module for selecting a recommended target user of an item to be recommended from the candidate target users according to the item information to be recommended and the user information of the candidate target user; and A target user push module is used for pushing a recommendation target user information to a user terminal of the recommender. A selection device for recommending target users, including: a processor; and a memory in which an executable instruction of the processor is stored; Wherein, the processor is configured to execute the steps of the method for selecting a recommended target user described in any one of request items 1 to 7 by executing the executable instruction. A computer-readable storage medium is used for storing a program, wherein when the program is executed, the steps of the method for selecting a recommended target user described in any one of request items 1 to 7 are implemented.

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