CN114401242A - User recommendation method, device, equipment and computer readable storage medium - Google Patents
User recommendation method, device, equipment and computer readable storage medium Download PDFInfo
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Abstract
The application discloses a user recommendation method, a user recommendation device, user recommendation equipment and a computer readable storage medium. The user recommendation method comprises the steps of obtaining service behavior information between a first user and other users in at least one service scene; determining at least one target user from the other users according to the service behavior information; and taking the at least one target user as a candidate recommending user of the first user in a target service scene. According to the embodiment of the application, the user recommendation of the business scene based on the light social product service system can be realized.
Description
Technical Field
The present application belongs to the field of computer technologies, and in particular, to a user recommendation method, apparatus, device, and computer-readable storage medium.
Background
With the development of internet technology and social networks, social products and business scenes are more and more. Currently, there are many user recommendations based on social products. For example, by reading an address book of a user, inquiring registration information of friends in the product of the address book of the user, and recommending the registration information to the user; or the interest points of the users are analyzed and mined, and based on the message square, topics and bloggers which are interested by the users are matched, and other related users are recommended to the users; or the user is guided to actively input the mobile phone numbers of other users, or the nicknames, the user IDs and the like in the product, and the other users are recommended to the user.
However, recommending users in the way of the address book has the risk of user privacy compliance, and the difficulty is high when the users authorize to read the address book; recommending based on user interests generally requires basic behavior data of users, such as interested topics and tasks, but for non-social products, the difficulty is high when the basic behavior data of the users are constructed and analyzed from zero; the mode that the user actively inputs the mobile phone numbers or the user IDs of other users is one of basic capabilities of strong social products, and the difficulty is higher for light social products. Therefore, in a business scenario of a light social product service system, it is difficult to perform user recommendation by the above scheme.
Disclosure of Invention
The embodiment of the application provides a user recommendation method, a user recommendation device, user recommendation equipment and a computer readable storage medium, and the user recommendation of a business scene based on a light social product service system can be realized.
In a first aspect, an embodiment of the present application provides a user recommendation method, where the method includes:
acquiring service behavior information between a first user and other users in at least one service scene;
determining at least one target user from the other users according to the service behavior information;
and taking the at least one target user as a candidate recommending user of the first user in a target service scene.
In a second aspect, an embodiment of the present application provides a user recommendation device, where the device includes:
the acquisition module is used for acquiring the business behavior information between the first user and other users in at least one business scene;
the determining module is used for determining at least one target user from the other users according to the service behavior information;
and the processing module is used for taking the at least one target user as a candidate recommending user of the first user in a target service scene.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
the steps of the user recommendation method as described in any of the embodiments of the first aspect are implemented when the processor executes the computer program instructions.
In a fourth aspect, the present application provides a computer-readable storage medium, on which computer program instructions are stored, where the computer program instructions, when executed by a processor, implement the steps of the user recommendation method as described in any one of the embodiments of the first aspect.
According to the user recommendation method, the user recommendation device, the user recommendation equipment and the computer readable storage medium in the embodiment of the application, the candidate recommended user of the first user in the target service scene is determined through the service interaction relationship between the first user and other users, namely according to the service behavior information of the first user and other users in at least one service scene, so that the user recommendation in the target service scene can be realized by utilizing the service behaviors of the users in different service scenes, and further the user recommendation of the service scene based on the light social product service system is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart illustrating an embodiment of a user recommendation method provided herein;
FIG. 2 is a flowchart illustrating a cross-platform user recommendation method according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart diagram illustrating another embodiment of a user recommendation method provided herein;
FIG. 4 is a schematic structural diagram of an embodiment of a user recommendation device provided in the present application;
fig. 5 is a schematic structural diagram of an embodiment of an electronic device provided in the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
When a light social product service system is constructed, user recommendation service needs to be provided for a certain service scene, namely analysis construction and recommendation service of friend relationships are involved. However, in the light social product service system, data of "social relations" are not maintained, so how to construct friend relations and recommend the friend relations to other users is a problem to be solved. In common internet social products, a friend relationship is generally established based on user identifications such as a mobile phone number or the like or user identifications of a social product APP, but in a light social product service system, such "social relationship" data does not exist, so that friend users needing to be recommended in a new service scene cannot be determined in an existing user recommendation mode.
In order to solve the prior art problems, embodiments of the present application provide a user recommendation method, apparatus, device, and computer-readable storage medium. First, a user recommendation method provided in the embodiment of the present application is described below.
Fig. 1 shows a flowchart of an embodiment of a user recommendation method provided in the present application. As shown in fig. 1, the user recommendation method may specifically include the following steps:
s110, acquiring service behavior information between the first user and other users in at least one service scene;
s120, determining at least one target user from other users according to the service behavior information;
s130, taking at least one target user as a candidate recommending user of the first user in the target service scene.
Therefore, candidate recommended users of the first user in the target service scene are determined through the service interaction relationship between the first user and other users, namely according to the service behavior information of the first user and other users in at least one service scene, so that the user recommendation in the target service scene can be realized by using the service behaviors of the users in different service scenes, and further the user recommendation of the service scene based on the light social product service system is realized.
Specific implementations of the above steps are described below.
In some embodiments, in S110, the first user may be a user to receive a user recommendation, in other words, other users may be recommended to the first user, so that the first user selects a user needed from the recommended users to perform operations such as adding a friend or performing service interaction. The other users may include users, except the first user, in at least one specific service scenario and having service interaction with the first user, where the service scenario includes, but is not limited to, a transfer scenario, a transaction scenario, a recharge scenario, a payment scenario, a pull-new scenario, and the like. It should be noted that, other users may generate the service interaction behavior with the first user only in one service scenario, or may generate the service interaction behavior with the first user in a plurality of service scenarios, respectively, which is not limited herein.
Illustratively, according to the service interaction behavior between the first user and other users in each service scenario, corresponding service behavior information may be obtained through statistics, where the service behavior information may include at least one of a number of service interactions and a service interaction time interval. Here, the interaction time interval may include a time from the last time the interaction was generated, or may include an average value of all the interaction time intervals.
As an example, in the case that the service scenario is a transfer scenario, if the first user transfers money to the a user, it may be considered that the first user has a service interaction with the a user in the transfer scenario. If the first user transfers accounts to the user A for 2 times in total, wherein the first transfer time and the second transfer time are separated by one month, the business behavior information of the first user and the user A under the transfer scene can be obtained through statistics, specifically, the account is transferred for 2 times, and the transfer time is separated by one month.
In some embodiments, the other users include users having a direct business interaction relationship with the first user and users having an indirect business interaction relationship with the first user.
Here, the direct service interaction relationship may be understood as that a direct service interaction behavior exists between the first user and other users; indirect business interaction is understood to mean that, although the first user and other users do not directly have business interaction behavior, there are users with whom the business interaction behavior occurs in common between the first user and other users. In other words, if the first user and the a user have business interaction behavior, and the B user and the a user also have business interaction behavior, the first user and the B user may be considered to have indirect business interaction relationship.
For example, in a top-up scenario, the service interaction behavior may be a top-up behavior. If the user A has recharged the user B and the user B has recharged the user C, it can be considered that a direct service interaction relationship exists between the user A and the user B and an indirect service interaction relationship exists between the user A and the user C.
Therefore, all users having a service interaction relationship with the first user can be identified by acquiring not only other users having a direct service interaction relationship with the first user but also other users having an indirect service interaction relationship with the first user, so that a friend relationship can be more comprehensively established, and the range of recommendable users is wider.
In some embodiments, in S120, the target user and the first user may have direct or indirect service interaction behaviors in at least one service scenario, and the target user may be a user that needs to be recommended to the first user.
For example, since there may be a plurality of other users having service interaction with the first user in the at least one service scenario, a target user that can be recommended to the first user in the target service scenario may be selected from a plurality of other users, where the target user may be one or a plurality of users. Of course, all users having a service interaction behavior with the first user in the at least one service scenario may also be determined as target users to be recommended, which is not limited herein.
In some embodiments, in S130, the target service scenario may be another scenario other than the at least one service scenario. In the target service scenario, one or more users determined by service interaction behaviors among users in the at least one service scenario may be used as target users to be recommended to the first user. Therefore, the first user can select a needed user from at least one recommended target user to perform friend adding operation or service interaction operation in a target service scene.
It should be noted that, when recommending other users to the first user in the target service scenario based on the service interaction behavior between users in at least one service scenario, user relationship mining may be performed based on the same platform, that is, the at least one service scenario and the target service scenario may belong to different scenarios in the same platform, for example, a transfer scenario and a recharge scenario in a certain payment software.
In addition, the friend relationship across platforms can be constructed in a mode of platform outer circulation. In some embodiments, the at least one service scenario may include a first service scenario in the target platform, and the other users may include at least one second user. Based on this, in order to implement cross-platform user recommendation, in some embodiments, the S110 may specifically include:
determining at least one second user associated with the first user through a link corresponding to the first service scene;
and acquiring business behavior information between the first user and the at least one second user according to the business behavior of the first user and the at least one second user based on the link.
Here, the first service scenario may be one of the service scenarios in the third party platform, that is, the target platform, or may be one of the service scenarios in the present platform, but the service scenario is displayed by the target platform. The target platform may be a third party platform other than the present platform. In addition, the link corresponding to the first service scenario may be a link displayed or shared by the first user or the second user based on a target platform, for example, a link shared by the first user or the second user in a pull-up scenario invites the new user, or a preferential activity link shared by the first user or the second user in a recharge and payment scenario, and the like.
For example, the way in which the first user and the second user are associated through the link includes, but is not limited to, a way in which the first user shares the link and the second user clicks the link, a way in which the second user shares the link and the first user clicks the link, a way in which both the first user and the second user click the same link shared by other users, and the like.
Additionally, link-based business activities include, but are not limited to, clicking on a link, transferring money through a link, registering with a link, and the like.
The second user may be a user already registered in the present platform, or may be a user not registered in the present platform.
As an example, if the first user is a registered user of the platform and the second user is not a registered user of the platform, when the second user is associated with the first user by clicking a link shared by the first user, the platform may guide authorization of the second user through the prompt message to obtain user information of the second user, so that the second user becomes a registered user of the platform. The number of the second users associated with the first user through the link may be one or more, and is not limited herein.
In some embodiments, the link is a link which is shared by the first user through the target platform and is clicked by the second user and corresponds to the first service scenario; or the link is shared by the second user through the target platform and clicked by the first user and corresponds to the first service scene.
In other words, the first user and the second user are associated through the link, which may be understood as that the first user shares the link with the second user and the second user clicks the link, or that the second user shares the link with the first user and the first user clicks the link.
In some specific examples, as shown in fig. 2, a user a in a P scene of the present platform shares information of the P scene and information of the user a in the link to a friend circle or a group chat of the W platform in a manner of sharing a P scene link. If the user B in the platform W clicks the link shared by the user A, the background service provided by the scene P can acquire the unionId of the user B on the platform W, or can acquire the mobile phone number of the user B through guide authorization, wherein the unionId can be a user identity which is agreed between the platform and the platform W and can uniquely represent the user identity. After the uinoId or the mobile phone number of the user B is obtained, the user ID of the user B on the platform can be inquired in the background of the scene P, and if the user ID of the user B on the platform is inquired, the one-time interaction relationship between the user A and the user B can be established in the scene P. After the interaction relationship between the user A and the user B occurs, the head portrait and the nickname of the user B can be inquired, if the head portrait and the nickname of the user B can be inquired, the page task is displayed, and if the head portrait and the nickname of the user B cannot be inquired, authorization is applied, the head portrait and the nickname in the platform are updated, and the page task is displayed finally. If the background of the platform can not inquire the user ID of the user B in the scene P, recording the unioniD and the mobile phone number of the user B in the platform W, and then guiding the user B to register as the user in the scene P of the platform. Once the user B is successfully registered, the user ID, the unionId and the mobile phone number information of the user B in the scene P are maintained by the platform, and further the friend relationship between the user A and the user B can be successfully established. In addition, as the unionId, the user ID and the mobile phone number of the user a and the user B are maintained in the platform under the scenario P, for a new scenario Q in the platform, the friend relationship between the user a and the user B can be quickly established in the scenario Q as long as the right for querying the data associated with the unionId and the mobile phone number of the scenario P can be obtained.
Therefore, the service interaction relationship in the target platform can be imported into the platform by acquiring the service behavior information of the first user and the second user on the target platform, so that cross-platform user recommendation is realized.
In order to quickly construct a social relationship in a target service scene, as another implementation manner of the present application, the present application further provides another implementation manner of a user recommendation method, which is specifically referred to in the following embodiments.
Referring to fig. 3, another implementation manner of the user recommendation method provided by the present application includes the following steps: S110-S130, which is explained in detail below.
S110, acquiring business behavior information between the first user and other users in at least one business scene.
And S121, determining a relationship strength value between the first user and each of the other users according to the service behavior information.
Here, the strength of relationship value may be a specific value that can represent the strength of the relationship between two users. In a business scenario, users all have strength of relationship values. The calculation of the relationship strength value is closely related to the interaction times, the average interaction time interval, the interval between the current time and the latest interaction time and the like. The relation strength value is in direct proportion to the number of interactions, in inverse proportion to the average interaction time interval and in inverse proportion to the time interval from the last time of interaction.
Illustratively, when the friend relationship is constructed based on inventory data analysis, the strength value of the relationship between the users can be calculated through a preset calculation formula.
In addition, since the association degrees between the service scenes are not the same, in order to improve the accuracy of the strength-of-relationship value, in some embodiments, the S121 may specifically include:
determining an initial relationship strength value between the first user and each of other users respectively according to the service behavior information;
and carrying out weighted summation on the initial relation strength value based on the weight values of at least one service scene corresponding to the target service scene respectively to obtain the relation strength value between the first user and each user respectively.
Here, the initial relationship strength value may be obtained by directly assigning values to different service behavior information by platform staff according to different service scenarios and corresponding experience values. One of the principles in assigning may be a strength of relationship between users having direct service interaction relationship, which needs to be greater than a strength of relationship between users having indirect service interaction relationship. Of course, a corresponding calculation formula may also be constructed based on the preset rule, and the data in the service behavior information is substituted into the calculation formula to calculate to obtain the corresponding relationship strength value. The predetermined rules include, but are not limited to, proportional to the number of interactions, inversely proportional to the average interaction time interval, and inversely proportional to the time interval from the last time the interaction was generated.
For example, the initial relationship strength value may be calculated according to the number of service interactions between the user a and the user B in the M scene, and when the number of service interactions between the user a and the user B in the M scene is M', the initial relationship strength value between the user a and the user B in the M scene is M according to a formula.
In addition, since the weights of different service scenarios relative to the target service scenario are different, the weights of the service scenarios need to be considered when calculating the strength of relationship values between the first user and other users. The weighted values corresponding to the target service scenario may be different or the same for different service scenarios.
Illustratively, if the initial strength of relationship value of the A user and the B user in the M scene is M, the initial strength of relationship value of the A user and the C user in the M scene is M1, the initial strength of relationship value of the A user and the B user in the N scene is N, and the initial strength of relationship value of the A user and the C user in the N scene is N1. Taking the calculation of the relationship strength value between the user a and the user B in the P scene as an example, the calculation formula can be represented as: and P (strength of relationship in the P scene) — (weight of M scene relative to P scene) × M + (weight of N scene relative to P scene) × N, wherein P is the strength of relationship between the a user and the B user in the P scene. Also, the strength of relationship between the a user and each user can be calculated by the same calculation method as described above.
On the other hand, if the direct interaction relationship does not exist between the user a and the user C, but the indirect interaction relationship exists, the strength of relationship between the user a and the user C can be obtained through the common neighbor analysis. For example, if the user a and the user B are in a friend relationship, and the user B and the user C are in a friend relationship, the user a and the user C may be said to be in a potential friend relationship, that is, the relationship strength value between the user a and the user C may be calculated according to the relationship strength value between the user a and the user B and the relationship strength value between the user B and the user C. Or the number of common friends between the user A and the user C can be determined as the strength of relationship value of the user A and the user C.
Therefore, corresponding weight values are set according to different service scenes, the initial relation strength value is weighted and summed, the relation strength value is calculated, and therefore the accuracy of the calculation result of the relation strength value can be improved.
Based on this, in order to specifically determine the weight value of the target service scenario corresponding to at least one service scenario, in some embodiments, before the foregoing step, the method may further include:
and determining weight values respectively corresponding to the at least one service scene according to the incidence relation between the at least one service scene and the target service scene.
Here, the weights between different service scenarios can be set by means of empirical value assignment. Of course, the weight between different scenes can also be calculated by constructing a scene incidence relation analysis model. Specifically, the weight value between the scenes may be calculated based on the strength of the association relationship between the scenes. The incidence relation among the scenes can be obtained through analysis of historical user recommendation success rate, data source incidence and the like.
For example, when the historical success rate of recommending the user to the P scene by the user interaction behavior data in the M scene is higher than the historical success rate of recommending the user to the P scene by the user interaction behavior data in the N scene, the weight value of the M scene relative to the P scene is higher than the weight value of the N scene relative to the P scene. For example, if the history success rate of recommending the user to the P scene by the M scene is 60%, and the history success rate of recommending the user to the P scene by the N scene is 40%, the weight value of the M scene relative to the P scene may be determined to be 0.6, and the weight value of the N scene relative to the P scene may be determined to be 0.4.
Therefore, by determining the weight values between at least one service scene and the target service scene according to the association relationship between different service scenes, the corresponding weight values can be adaptively determined according to the association degree between different service scenes, and the accuracy of the calculation result of the relationship strength value can be further improved.
And S122, ranking each user according to the relationship strength value to obtain a ranking result.
Here, the relationship strength values may be ranked from large to small, the ranking number and the corresponding relationship strength value of each user are obtained, and a corresponding user recommendation list is generated.
And S123, determining at least one target user based on the arrangement result.
Here, all the users having the strength of relationship with the first user may be determined as candidate recommending users, and the sequence to be recommended may be consistent with the ranking number of each user. Or, a relationship strength threshold may be preset, and only when the relationship strength value is greater than the threshold, the user corresponding to the relationship strength value may be used as the candidate recommended user. Therefore, a user recommendation list of the first user in the target scene can be obtained. All users in the user recommendation list can be used as target users.
S130, taking at least one target user as a candidate recommending user of the first user in the target service scene.
Wherein S110 and S130 are the same as the above embodiments and will not be described in detail herein for the sake of brevity.
Therefore, the relationship strength value between the target user and other users is calculated according to the interaction behavior data of the users in at least one service scene, the arrangement is carried out based on the strength value, and at least one target user is determined from the arrangement result, so that a user recommendation list which can be started from zero can be quickly obtained from other service scenes in the target service scene without social records, and the social relationship corresponding to the target user in the target service scene can be quickly established in a light social product system.
Based on the same inventive concept, the application also provides a user recommendation device. The details are described with reference to fig. 4.
Fig. 4 shows a schematic structural diagram of an embodiment of a user recommendation device provided by the present application.
As shown in fig. 4, the user recommendation apparatus 400 may include:
an obtaining module 401, configured to obtain service behavior information between a first user and other users in at least one service scenario;
a determining module 402, configured to determine at least one target user from other users according to the service behavior information;
and the processing module 403 is configured to use at least one target user as a candidate recommended user of the first user in the target service scenario.
The following describes the user recommendation device 400 in detail, specifically as follows:
in some embodiments, the determining module 402 may specifically include:
the first determining submodule is used for determining the strength value of the relationship between the first user and each of the other users according to the business behavior information;
the arrangement submodule is used for arranging each user according to the relation strength value to obtain an arrangement result;
and the second determining submodule is used for determining at least one target user based on the arrangement result.
In some embodiments, the first determining sub-module may specifically include:
the first determining unit is used for determining an initial relationship strength value between the first user and each of the other users according to the service behavior information;
and the summing unit is used for weighting and summing the initial relation strength value based on the weight values of at least one service scene corresponding to the target service scene respectively to obtain the relation strength value between the first user and each user respectively.
In some embodiments, as described above, the first determining sub-module may further include:
and the second determining unit is used for determining the weight values respectively corresponding to the at least one service scene according to the incidence relation between the at least one service scene and the target service scene.
In some of these embodiments, the other users include users having a direct business interaction relationship with the first user and users having an indirect business interaction relationship with the first user.
In some embodiments, the obtaining module 401 may specifically include:
a third determining submodule, configured to determine at least one second user associated with the first user through a link corresponding to the first service scenario;
and the obtaining submodule is used for obtaining the business behavior information between the first user and the at least one second user according to the business behavior of the first user and the at least one second user based on the link.
In some embodiments, the link is a link which is shared by the first user through the target platform and clicked by the second user and corresponds to the first service scene; or the link is shared by the second user through the target platform and is clicked by the first user and corresponds to the first service scene.
In some of these embodiments, the service behavior information includes at least one of a number of service interactions and a service interaction time interval.
Therefore, candidate recommended users of the first user in the target service scene are determined through the service interaction relationship between the first user and other users, namely according to the service behavior information of the first user and other users in at least one service scene, so that the user recommendation in the target service scene can be realized by using the service behaviors of the users in different service scenes, and further the user recommendation of the service scene based on the light social product service system is realized.
Fig. 5 shows a hardware structure diagram of an embodiment of the electronic device provided in the present application.
The electronic device 500 may include a processor 501 and a memory 502 that stores computer program instructions.
Specifically, the processor 501 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory may include Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the application.
The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement any one of the user recommendation methods in the above embodiments.
In some examples, the electronic device 500 may also include a communication interface 503 and a bus 504. As shown in fig. 5, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 504 to complete communication therebetween.
The communication interface 503 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application.
The bus 504 includes hardware, software, or both to couple the components of the electronic device to one another. By way of example, and not limitation, the bus 504 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of these. Bus 504 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
Illustratively, the electronic device 500 may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like.
The electronic device 500 may execute the user recommendation method in the embodiment of the present application, so as to implement the user recommendation method and apparatus described in conjunction with fig. 1 to 4.
In addition, in combination with the user recommendation method in the foregoing embodiment, the embodiment of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the user recommendation methods in the above embodiments. Examples of computer-readable storage media include non-transitory computer-readable storage media such as portable disks, hard disks, Random Access Memories (RAMs), Read Only Memories (ROMs), erasable programmable read only memories (EPROMs or flash memories), portable compact disk read only memories (CD-ROMs), optical storage devices, magnetic storage devices, and so forth.
It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.
Claims (11)
1. A user recommendation method, comprising:
acquiring service behavior information between a first user and other users in at least one service scene;
determining at least one target user from the other users according to the service behavior information;
and taking the at least one target user as a candidate recommending user of the first user in a target service scene.
2. The method of claim 1, wherein determining at least one target user from the other users according to the traffic behavior information comprises:
determining a relationship strength value between the first user and each of the other users according to the service behavior information;
according to the relation strength value, ranking each user to obtain a ranking result;
at least one target user is determined based on the ranking results.
3. The method according to claim 2, wherein the determining, according to the service behavior information, a strength of relationship value between the first user and each of the other users respectively comprises:
determining an initial relationship strength value between the first user and each of the other users respectively according to the service behavior information;
and performing weighted summation on the initial relation strength value based on the weight values of the at least one service scene corresponding to the target service scene respectively to obtain the relation strength value between the first user and each user respectively.
4. The method according to claim 3, wherein before performing weighted summation on the initial strength of relationship values based on the weight values of the at least one service scenario respectively corresponding to the target service scenarios to obtain the strength of relationship values between the first user and each of the users respectively, the method further comprises:
and determining weight values respectively corresponding to the at least one service scene according to the incidence relation between the at least one service scene and the target service scene.
5. The method of claim 1, wherein the other users comprise users having a direct business interaction relationship with the first user and users having an indirect business interaction relationship with the first user.
6. The method of claim 1, wherein the at least one service scenario comprises a first service scenario in a target platform, and the other users comprise at least one second user;
the acquiring of the service behavior information between the first user and other users in at least one service scenario includes:
determining at least one second user associated with the first user through a link corresponding to the first service scene;
and acquiring business behavior information between the first user and the at least one second user according to the business behavior of the first user and the at least one second user based on the link.
7. The method according to claim 6, wherein the link is a link corresponding to the first service scenario, which is shared by the first user through the target platform and clicked by the second user; alternatively, the first and second electrodes may be,
the link is shared by the second user through the target platform and clicked by the first user and corresponds to the first service scene.
8. The method according to any of claims 1-7, wherein the service behavior information comprises at least one of a number of service interactions and a service interaction time interval.
9. A user recommendation apparatus, the apparatus comprising:
the acquisition module is used for acquiring the business behavior information between the first user and other users in at least one business scene;
the determining module is used for determining at least one target user from the other users according to the service behavior information;
and the processing module is used for taking the at least one target user as a candidate recommending user of the first user in a target service scene.
10. An electronic device, characterized in that the device comprises: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, performs the steps of the user recommendation method of any of claims 1-8.
11. A computer-readable storage medium, having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the user recommendation method of any of claims 1-8.
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