CN111191143B - Application recommendation method and device - Google Patents
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Abstract
The embodiment of the application provides an application recommendation method and device. The method comprises the following steps: acquiring the intimacy between a target user and an associated user thereof; according to the feature data of the associated user, determining the interest degree of the associated user for the application to be recommended; determining a recommendation index of the application to be recommended for the associated user according to the intimacy between the target user and the associated user and the interestingness of the associated user for the application to be recommended; recommending the application to be recommended to the associated user based on the recommendation index of the application to be recommended to the associated user. The technical scheme of the embodiment of the application can realize the efficient recommendation of the application.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to an application recommendation method, an apparatus, a terminal device, and a computer readable storage medium.
Background
Currently, applications suitable for terminal devices (such as smartphones, tablet computers or personal computers) are increasing, and in order to enable applications of interest to users to reach users accurately, the user plane of the applications is enlarged, so that it is necessary to recommend the applications to more users.
In the prior art, in order to reduce the interference of application recommendation to users, the user initiates experience invitation of the application to be recommended to friends of the user, so that popularization of the application to be recommended is realized according to mutual propagation between the user and the friends of the user, but the number of friends of the user is often large, whether the application to be recommended can be accurately recommended to the friends with higher possibility of accepting the experience invitation, and the recommendation efficiency of the application to be recommended is determined.
Disclosure of Invention
In order to accurately recommend an application to be recommended to a user with higher acceptance, the embodiment of the application provides an application recommendation method, an application recommendation device, terminal equipment and a computer readable storage medium, which are used for improving the recommendation efficiency of the application to be recommended.
The technical scheme adopted by the application is as follows:
an application recommendation method, comprising: acquiring the intimacy between a target user and an associated user thereof; according to the feature data of the associated user, determining the interest degree of the associated user for the application to be recommended; determining a recommendation index of the application to be recommended for the associated user according to the intimacy between the target user and the associated user and the interestingness of the associated user for the application to be recommended; recommending the application to be recommended to the associated user based on the recommendation index of the application to be recommended to the associated user.
An application recommendation device, comprising: the affinity acquisition module is used for acquiring the affinity between the target user and the associated user; the interest degree acquisition module is used for determining the interest degree of the associated user for the application to be recommended according to the characteristic data of the associated user; the recommendation index acquisition module is used for determining a recommendation index of the application to be recommended for the associated user according to the intimacy degree between the target user and the associated user and the interest degree of the associated user for the application to be recommended; and the application recommendation module is used for recommending the application to be recommended to the associated user based on the recommendation index of the application to be recommended for the associated user.
A terminal device comprising a processor and a memory having stored thereon computer readable instructions which, when executed by the processor, implement an application recommendation method as described above.
A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform an application recommendation method as described above.
According to the technical scheme, the affinity between the target user and the associated user is obtained, and the interest degree of the associated user for the application to be recommended is determined according to the characteristic data of the associated user, so that the recommendation index of the application to be recommended for the associated user is determined according to the affinity between the target user and the associated user and the interest degree of the associated user for the application to be recommended. Because the recommendation index of the application to be recommended for the associated user combines the intimacy between the associated user and the target user and the interestingness of the associated user for the application to be recommended, the recommendation index of the associated user for the application to be recommended can accurately reflect the acceptance degree of the associated user for the application to be recommended, namely the possibility that the associated user accepts the experience invitation initiated by the target user, so that the application to be recommended is recommended to the associated user based on the recommendation index of the application to be recommended for the associated user, and high-efficiency recommendation of the application to be recommended can be realized.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 is a schematic illustration of an implementation environment in which embodiments of the present application are directed;
FIG. 2 is a hardware block diagram of the intelligent terminal 100 in the implementation environment shown in FIG. 1 in one embodiment;
FIG. 3 is a flowchart illustrating an application recommendation method, according to an example embodiment;
FIG. 4 is a schematic diagram of an application recommendation interface, shown in accordance with an exemplary embodiment;
FIG. 5 is a schematic diagram of an application recommendation interface, shown in accordance with another exemplary embodiment;
FIG. 6 is a flow chart of step 210 of FIG. 3 in one embodiment;
FIG. 7 is a flowchart illustrating an application recommendation method according to another exemplary embodiment;
FIG. 8 is a flowchart illustrating an application recommendation method according to another exemplary embodiment;
FIG. 9 is a schematic diagram of an application scenario illustrated according to an example embodiment;
fig. 10 is a block diagram illustrating an application recommendation device according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
Referring to fig. 1, fig. 1 is a schematic diagram of an implementation environment according to the present application. As shown in fig. 1, the implementation environment is a social network, and the social network includes a plurality of intelligent terminals 100 (only 4 are shown in fig. 1).
The intelligent terminal 100 is used to run various applications, and the intelligent terminal 100 may be used to run social applications such as WeChat, QQ, etc., or may be used to run game applications such as glory, happy mahjong, etc., or other types of applications, which are not limited herein.
The intelligent terminals 100 are pre-connected by a communication module configured by themselves to establish wired or wireless network connection, so that communication between the intelligent terminals 100 is realized through the network connection, and users corresponding to the intelligent terminals 100 are associated through the pre-established network connection between the intelligent terminals 100.
The smart terminal 100 may be a smart phone, a tablet computer, a notebook computer, a computer, or any other electronic device capable of running the above applications, which is not limited herein.
In order to expand the user plane that the application to be recommended can reach, it is generally necessary to propagate the application to be recommended between associated users by means of association between different users, so as to realize popularization of the application to be recommended. As shown in FIG. 1, users 2-4 are all associated users of user 1, e.g., users 2-4 are friends of user 1 on a social application, and user 1 can recommend an application to be recommended to user 2-4, i.e., recommend the application to be recommended to the associated user of user 1, by sending an experience invitation of the application to be recommended to user 2-4.
In an actual application scenario, the number of other users associated with a certain user is often very large, for example, the number of WeChat friends of a common user can reach hundreds, and when the user sends experience invitations of applications to be recommended to the associated user, in order to consider the recommendation will of the user, the efficiency of application recommendation and the equipment resources required to be consumed by application recommendation, only the experience invitations of the applications to be recommended are sent to part of the associated user, but not all the associated users are recommended to the applications to be recommended.
If the user sends the experience invitation of the application to be recommended, the associated user has higher acceptance degree on the application to be recommended, and the associated user has higher possibility of using the application to be recommended, thereby achieving better recommendation effect. Therefore, if the application to be recommended is recommended to the associated user with higher acceptance, the recommendation efficiency of the application to be recommended is greatly improved.
Referring to fig. 2, fig. 2 is a block diagram of an intelligent terminal according to an exemplary embodiment.
It should be noted that the intelligent terminal 100 is only an example adapted to the present application, and should not be construed as providing any limitation on the scope of use of the present application. Nor should the intelligent terminal be construed as necessarily relying on or necessarily having one or more of the components of the exemplary intelligent terminal 100 shown in fig. 2.
As shown in fig. 2, the intelligent terminal 100 includes a memory 101, a memory controller 103, one or more processors 105, a peripheral interface 107, a radio frequency module 109, a positioning module 111, a camera module 113, an audio module 115, a touch screen 117, and a key module 119. These components communicate with each other via one or more communication buses/signal lines 121.
The memory 101 may be configured to store a computer program and a module, such as computer readable instructions and modules corresponding to the method and apparatus for controlling multi-party call in the exemplary embodiment of the present application, and the processor 105 executes the computer readable instructions stored in the memory 101, thereby performing various functions and data processing, that is, completing the application recommendation method.
Memory 101, which is the carrier of resource storage, may be random access memory, e.g., high speed random access memory, non-volatile memory, such as one or more magnetic storage devices, flash memory, or other solid state memory. The storage means may be a temporary storage or a permanent storage.
The peripheral interface 107 may include at least one wired or wireless network interface, at least one serial-to-parallel conversion interface, at least one input/output interface, at least one USB interface, etc. for coupling external various input/output devices to the memory 101 and the processor 105 to enable communication with the external various input/output devices.
The radio frequency module 109 is configured to receive and transmit electromagnetic waves, and to implement mutual conversion between the electromagnetic waves and the electrical signals, so as to communicate with other devices through a communication network. The communication network may include a cellular telephone network, a wireless local area network, or a metropolitan area network, and may employ various communication standards, protocols, and techniques.
The positioning module 111 is configured to obtain a current geographic location of the intelligent terminal 100. Examples of the positioning module 111 include, but are not limited to, global satellite positioning system (GPS), wireless local area network or mobile communication network based positioning technology.
The camera module 113 is attached to a camera for taking pictures or videos. The photographed pictures or videos may be stored in the memory 101, and may also be transmitted to an upper computer through the rf module 109.
The audio module 115 provides an audio interface to the user, which may include one or more microphone interfaces, one or more speaker interfaces, and one or more earphone interfaces. The interaction of the audio data with other devices is performed through the audio interface. The audio data may be stored in the memory 101 or may be transmitted via the radio frequency module 109.
The touch screen 117 provides an input-output interface between the intelligent terminal 100 and the user. Specifically, the user may perform an input operation, such as a gesture operation of clicking, touching, sliding, etc., through the touch screen 117, so that the intelligent terminal 100 responds to the input operation. The intelligent terminal 100 displays and outputs the output content formed by any one form or combination of the text, the picture or the video to the user through the touch screen 117.
The key module 119 includes at least one key to provide an interface for a user to input to the intelligent terminal 100, and the user can perform different functions by pressing different keys of the intelligent terminal 100. For example, the sound adjustment key may allow the user to adjust the volume of sound played by the smart terminal 100.
It is to be understood that the structure shown in fig. 2 is merely illustrative, and that the intelligent terminal 100 may also include more or fewer components than shown in fig. 2, or have different components than shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
Referring to fig. 3, fig. 3 is a flowchart illustrating an application recommendation method according to an exemplary embodiment. The method is applicable to the intelligent terminal 100 in the implementation environment shown in fig. 1, and the structure of the intelligent terminal 100 can be as shown in fig. 2.
As shown in fig. 3, the application recommendation method may include the steps of:
step 210, obtaining the intimacy between the target user and the associated user.
As described above, in order to enable an application of interest to a user to accurately reach the user while enlarging the user plane of the application, it is necessary to recommend the application to more users.
For applications to be recommended, the applications to be recommended are generally required to be spread among the associated users by means of association among different users, so that the applications to be recommended are promoted among the associated users.
The target user is used as a recommendation initiator of the application to be recommended, and the application to be recommended needs to be recommended to the associated user, for example, an experience invitation of the application to be recommended is sent to the associated user so as to invite the associated user to experience the application to be recommended.
The associated user of the target user may be a user associated with the target user in one or more other applications run by the intelligent terminal, it being understood that the other applications described in this embodiment are applications other than the application to be recommended.
For ease of understanding, for example, in the implementation environment shown in fig. 1, if the user 1 is used as the target user in the application to be recommended, the users 2-4 may be not only friend users of the user 1 in some other application, but also friend users of the user 1 on different applications respectively.
Because the number of associated users associated with the target user is large, the target user typically sends experience invitations of the application to be recommended to some or a few associated users, and in order to promote the recommendation effect of the application to be recommended, it is necessary to accurately recommend the application to be recommended to the associated user with a high acceptance degree for the application to be recommended. Therefore, how to acquire the acceptance degree of the associated user of the target user for the application to be recommended is a key for accurately and efficiently recommending the application to be recommended.
In this embodiment, the higher the affinity between the target user and its associated user, the higher the possibility that the target user and the associated user use the same type of application, the greater the possibility that the associated user experiences the application to be recommended after receiving the experience invitation of the application to be recommended sent by the target user, and the greater the intention that the target user recommends the application to be recommended to the associated user.
Therefore, considering that the intimacy between the target user and the associated user has a great influence on the recommendation efficiency of the application to be recommended, the embodiment needs to acquire the intimacy between the target user and the associated user aiming at the target user to be subjected to application recommendation.
And 230, determining the interest degree of the associated user for the application to be recommended according to the characteristic data of the associated user.
The interest degree of the associated user in the application to be recommended refers to the interest degree of the associated user in the application to be recommended.
Considering that the likelihood that the associated user uses the application to be recommended after receiving the experience invitation of the application to be recommended sent by the target user is also greater under the condition that the interest degree of the associated user to the application to be recommended is greater, in this embodiment, it is necessary to acquire the interest degree of the associated user to the application to be recommended so as to comprehensively consider the acceptance degree of each associated user to the application to be recommended by combining the intimacy degree between the target user and the associated user.
The feature data of the associated user may include attribute information of the associated user and usage data of the associated user for other applications. By way of example, the attribute information of the associated user may include user basic attributes such as gender, age, region, etc. of the associated user, which is not limited herein.
The usage data of the associated user for other applications may include social data of the associated user such as the number of friends in other applications, the number of instant messaging messages sent and received in other applications, and the like, and may also include fund flow data of the associated user such as the amount and the number of receipts sent and received in other applications, the amount and the number of red packets sent and received, and may also include data such as the name of the associated user in other applications, the type of application used in other applications, and the like, which is not limited in this respect.
Therefore, the feature data of the associated user can be collected from the application running by the intelligent terminal, and the interest degree of the associated user for the application to be recommended can be determined based on the collected feature data of the associated user. For example, if the application to be recommended is a game application, and the feature data of the associated user indicates that the associated user uses other game applications, the interest of the associated user in the application to be recommended should also be higher, and the likelihood that the associated user uses the application to be recommended after receiving the experience invitation of the user to be recommended initiated by the target user is also higher.
In an exemplary embodiment, the interestingness of the associated user for the application to be recommended, which is predicted by the interestingness prediction model, can be obtained by inputting the feature data of the associated user into the interestingness prediction model trained in advance.
In this embodiment, the interest level prediction of the associated user for the application to be recommended may be abstracted into a two-class machine learning problem, so that the interest level prediction model may be a common machine learning model such as a logistic regression model (LR, logistic Regression), a support vector machine model (SVM, support Vector Machine), a Decision Tree model (DT, precision Tree), and the like, which is not limited in this respect.
Step 250, determining a recommendation index of the application to be recommended for the associated user according to the intimacy between the target user and the associated user and the interestingness of the associated user for the application to be recommended.
The recommendation index of the application to be recommended for the associated user corresponds to the possibility that the associated user accepts the experience invitation about the application to be recommended to use the application to be recommended after receiving the experience invitation about the application to be recommended initiated by the target user, namely the acceptance degree of the associated user for the application to be recommended.
As described above, the affinity between the target user and the associated user and the interest degree of the associated user in respect of the application to be recommended will affect the acceptance degree of the associated user in respect of the application to be recommended, so after the affinity between the target user and the associated user and the interest degree of the associated user in respect of the user to be recommended are respectively obtained, the recommendation index of the application to be recommended in respect of the associated user can be determined by combining the obtained affinity and interest degree.
In one exemplary embodiment, the recommendation index for the application to be recommended for the associated user is derived based on a pre-trained recommendation index prediction model. The recommendation index predicted by the recommendation index prediction model for the associated user can be obtained by inputting the affinity between the target user and the associated user obtained in step 210 and the interest of the associated user for the application to be recommended obtained in step 230 as feature data into a pre-trained recommendation index prediction model.
In this embodiment, the recommendation index prediction model is also a pre-trained machine learning model, and is used for predicting a recommendation index of an application to be recommended for an associated user according to input feature data. Considering that linear superposition of the intimacy between the target user and the associated user and the interestingness of the associated user to the application to be recommended is not significant, the method is more suitable for carrying out conditional combination on the intimacy between the target user and the associated user and the interestingness of the associated user to the application to be recommended to obtain a recommendation index of the application to be recommended to the associated user. Therefore, in this embodiment, the recommendation index prediction model may be a decision tree model.
The recommendation index prediction model predicts the obtained recommendation index for the associated user, and correspondingly is that the recommendation index prediction model scores the acceptance degree of the associated user about the application to be recommended, for example, the score result is usually between 0 and 1, the higher the score result is, the higher the acceptance degree of the associated user about the application to be recommended is, and otherwise, the lower the acceptance degree of the associated user about the application to be recommended is.
And determining the recommendation index of the application to be recommended for the associated user according to the recommendation index prediction model trained in advance for each associated user of the target user, thereby obtaining the acceptance degree of each associated user for the application to be recommended.
Step 270, recommending the application to be recommended to the associated user based on the recommendation index of the application to be recommended to the associated user.
As described above, according to the recommendation index of the application to be recommended for the associated user, the acceptance degree of each associated user of the target user for the application to be recommended can be clearly known.
Therefore, based on the recommendation index of the application to be recommended for the associated user, the user to be recommended can be recommended to the associated user with higher acceptance degree, so that accurate recommendation of the application to be recommended is realized, and a better recommendation effect is achieved.
For example, when the target user selects the associated user, the intelligent terminal may select the associated user with the designated ranking as the candidate user according to the recommendation index ranking of the associated user, for example, the associated user with the recommendation index ranking ten times is displayed as the candidate user, and the target user may determine the candidate user to be recommended for the application by selecting all or part of the displayed associated users, so that the intelligent terminal triggers the selected candidate user to recommend the application to be recommended to the target user, for example, sends an experience invitation of the application to be recommended to the candidate users. In other embodiments, the selection of the associated users may also be done automatically by the intelligent terminal, which may directly select the associated users of the specified rank according to the recommendation index ranking of the associated users and send experience invitations to the applications to be recommended to those associated users. That is, the associated user selected by the intelligent terminal does not need to be displayed to the target user, so that the triggering selection operation of the target user on the associated user is omitted, and the recommending process of the application to be recommended is more intelligent.
It should be noted that, the sending of the experience invitation of the application to be recommended to the associated user by the target user may be performed by other applications based on the association relationship established between the target user and the associated user. For example, in the implementation environment shown in fig. 1, user 1 is still used as the target user in the application to be recommended, if user 2-4 is a WeChat friend of user 1, user 1 sends a WeChat message to user 2-4 to invite user 2-4 to experience the application to be recommended; if the user 4 is a QQ friend of the user 1, the user 1 sends a corresponding QQ message to the user 4.
Therefore, in the method provided by the embodiment, the associated users for recommending the application have higher acceptance degree for the application to be recommended, and the possibility that the associated users respond to the recommendation of the application to be recommended initiated by the target user is also higher, so that a good recommending effect can be achieved.
The recommendation of the application to be recommended by the target user is usually initiated via the triggering recommendation of the application to be recommended by the target user, so in an exemplary embodiment, the application recommendation method should further include a step of monitoring whether the target user triggers the recommendation of the application to be recommended before step 210, and if the target user monitors that the user triggers the recommendation of the application to be recommended, the target user needs to initiate the recommendation of the application to be recommended, so that the application to be recommended is recommended to the associated user who has higher acceptance degree by executing the content described in steps 210-270.
Whether the intelligent terminal triggers the recommendation monitoring of the application to be recommended for the target user or not can be realized based on the application to be recommended which is operated by the intelligent terminal. In the process of using the application to be recommended, if the target user monitors that the application to be recommended jumps to enter the set application recommendation interface, or monitors that an application recommendation button arranged in the application recommendation interface is triggered, the target user is monitored to trigger the recommendation of the application to be recommended.
In one embodiment, the intelligent terminal monitors that the application to be recommended enters an application recommendation interface through the jump of the operation of the target user, acquires the intimacy between the target user and the associated user, determines the interestingness of the associated user for the application to be recommended according to the characteristic data of the associated user, determines the recommendation index of each associated user to be recommended according to the intimacy between the target user and the associated user and the interestingness of the associated user for the application to be recommended, and selects the associated user with the designated ranking as a candidate user to display to the target user.
As shown in fig. 4, a display area of the application recommendation interface displays a plurality of candidate users for the target user to select, the ranking order of the candidate users corresponds to the ranking of the recommendation indexes, the target user can check more candidate users by clicking a previous page button or a next page button, and select a candidate user by clicking a display area of the candidate user, or click the selected candidate user again to cancel the selection of the candidate user. In addition, the selected candidate users are correspondingly displayed with the selected identifications, as shown in fig. 5. After the selected candidate users are determined, the target user clicks the 'determination' button, and then the transmission of experience invitations of the applications to be recommended to the selected candidate users can be triggered.
Referring to FIG. 6, FIG. 6 is a flow chart of step 210 in one embodiment.
As shown in fig. 6, in an exemplary embodiment, the associated user is a friend user of the target user on the specified social application, for example, the associated user is a WeChat friend of the target user, and step 210 may include the steps of:
step 211, obtaining evaluation values of each social dimension of the target user and the associated user in the designated social application.
In this embodiment, the associated user is a friend user of the target user on the specified social application, which means that both the associated user and the target user are users on the specified social application, and therefore, the initiation of the experience invitation of the application to be recommended by the target user to the associated user should also be performed based on the specified social application. In executing the recommendation of the application to be recommended, attention should be paid to the affinity between the target user and the associated user on the specified social application.
Each social dimension of the target user and the associated user in the specified social application corresponds to a different social way of the target user and the associated user in the specified social application. By way of example, the social dimension of the specified social application may include at least one of a messaging dimension of instant messaging, an interaction dimension on a content sharing platform provided by the specified social application, an article reading dimension, and an application usage dimension.
It should be noted that, the dimension of sending and receiving the instant communication message corresponds to the sending and receiving of the instant communication message of the target user and the associated user on the specified social application, for example, if the specified social application is a WeChat, the dimension of sending and receiving the instant communication message corresponds to the WeChat message sent and received between the target user and the associated user.
The interaction dimension on the content sharing platform provided by the appointed social application corresponds to the content sharing initiated by the target user participating in the content sharing platform by the associated user, and the appointed social application is still taken as a WeChat for example.
The article reading dimension corresponds to the reading of articles by the target user and the associated user on the specified social application, which may be, for example, articles read in a WeChat subscription number.
The application usage dimension then corresponds to the usage of other applications by the target user and associated users, which should be associated with the specified social application. Still taking the specific social platform as a WeChat for example, the application program used by the target user and the associated user may be a WeChat applet or other application program logged in using a WeChat account, which is not limited herein.
The evaluation values of the target user and the associated user in the respective social dimensions of the specified social application correspond to the affinity of the target user and the associated user in the respective social dimensions.
In one exemplary embodiment, where the social dimension includes a transceiving dimension of the instant communication message, an evaluation value of the transceiving dimension of the instant communication message may be calculated from message data transmitted by the target user to the associated user and the number of messages transmitted by the associated user to the target user. The calculation formula of the evaluation value of the transceiving dimension of the instant messaging message is as follows:
wherein A represents a target user in the application to be recommended, and B represents an associated user of the target user. Will beThe 1 is added to avoid the case where the number of instant communication messages sent by the target user a to the associated user B is zero. If->If the value is larger than 1, the evaluation value of the receiving and transmitting dimension of the instant communication message can be obtained to be 1. If the number of instant communication messages sent by the associated user B to the target user A is greater than twice the number of instant communication messages sent by the target user A to the associated user B, multiplying the quotient of the two by 0.5 can be such that +.>The value is 1.
If the more replies the associated user B replies to the instant communication message sent by the target user A, the larger the evaluation value of the receiving and transmitting dimension of the instant communication message between the associated user B and the target user A is, the higher the affinity between the target user A and the associated user B is.
In another exemplary embodiment, where the social dimension includes an interaction dimension on a content sharing platform provided by a specified social application, an evaluation of this interaction dimension may be calculated based on the number of content shares initiated by the target user and the number of content shares initiated by the associated user participating in the target user. Illustratively, the calculation formula of the evaluation value of the interaction dimension on the content sharing platform provided by the specified social application is as follows:
as described previously, willThe addition of 1 is to avoid the situation where the target user a initiates the zero number of content shares. If the participation degree of the associated user B on the content sharing initiated by the target user A is higher, the higher the evaluation value of the interaction dimension of the associated user B and the target user A on the content sharing platform provided by the appointed social application is, the higher the affinity between the target user A and the associated user B is.
In another exemplary embodiment, where the social dimension includes an article reading dimension, the evaluation value of the article reading dimension may be calculated based on the number of articles read by the target user and the number of articles read by the associated user in conjunction with the target user. Illustratively, the evaluation value of the article reading dimension is calculated as follows:
As described above, adding 1 to the number of articles read by the target user a also serves to avoid the situation that the number of articles read by the target user a is zero. The greater the proportion of articles read by the target user a is, the greater the affinity between the target user a and the associated user B is.
In yet another exemplary embodiment, where the social dimension includes an application usage dimension, an evaluation value of the application usage dimension may be calculated based on the number of applications used by the target user and the number of applications used by the associated user in conjunction with the target user. Illustratively, the calculation formula of the evaluation value of the application using the dimension is as follows:
wherein adding 1 to the number of applications used by the target user a is still to avoid a situation where the number of applications used by the target user a is zero. The greater the proportion of the application program used by the associated user B to use the target user a, the greater the intimacy between the target user a and the associated user B is indicated.
The evaluation values of the target user and the associated user in the various social dimensions of the designated social application are obtained, so that the affinity of the target user and the associated user in the various social dimensions is obtained. Based on the affinities of the target user and the associated user in the social dimensions, the affinities of the target user and the associated user in the whole appointed social application can be obtained.
Step 213, calculating the intimacy between the target user and the associated user based on the weight of each social dimension and the evaluation values of the target user and the associated user in each social dimension.
Wherein the weight of each social dimension is preconfigured to represent the importance of each social dimension to the affinity between the target user and the associated user. Thus, based on the weight of each social dimension, the affinity of the target user and the associated user for the specified social application can be obtained by carrying out weighted sum operation on the evaluation values of the target user and the associated user in each social dimension.
Illustratively, the calculation formula for calculating the affinity between the target user and the associated user for the specified social application is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,weight corresponding to transmit-receive dimension representing instant messaging message, < ->Representing weights corresponding to interaction dimensions on a content sharing platform provided by the specified social application, +.>Representing the weight corresponding to the article reading dimension, +.>Representing the weight corresponding to the dimension used by the application.
Therefore, the embodiment considers the intimacy between the target user and the associated user on each social dimension of the designated social application, and can accurately obtain the intimacy between the target user and the associated user about the designated social application by carrying out weighted sum operation on the evaluation values of the target user and the associated user in each social dimension.
In further exemplary embodiments, as shown in fig. 7, the application recommendation method may further include the steps of:
step 310, generating positive sample data according to user data interested in the application to be recommended, and generating negative sample data according to user data not interested in the application to be recommended;
step 330, training the interestingness prediction model according to the positive sample data and the negative sample data.
In order to obtain an accurate interestingness prediction model, proper training data needs to be found to train the interestingness prediction model.
In this embodiment, the user of the application to be recommended is taken as a positive sample of interest in the application to be recommended, and the user of the application not to be recommended is taken as a negative sample of interest in the application to be recommended, so that the user data of interest in the application to be recommended is obtained as positive sample data, and the user data of interest in the application to be recommended is obtained as negative sample data.
It should be noted that, the user data that is interested or not interested in the application to be recommended refers to the feature data corresponding to the user, and as described above, the feature data may include attribute information of the user and usage data of other applications by the user, which is not described herein.
Therefore, the positive sample data which is interested in the application to be recommended and the negative sample data which is not interested in the application to be recommended are selected respectively to train the interestingness prediction model, so that the interestingness prediction model obtained through training can accurately predict the interestingness of the associated user to the application to be recommended according to the input characteristic data of the associated user.
In further exemplary embodiments, as shown in fig. 8, the application recommendation method may further include the steps of:
step 410, generating positive sample data according to user data successfully recommended by the application to be recommended, and generating negative sample data according to user data failed in recommending by the application to be recommended, wherein the user data comprises the interestingness of the user for the application to be recommended and the intimacy between the user and the associated user;
step 430, training the recommendation index prediction model according to the positive sample data and the negative sample data.
Similar to the training of the interestingness prediction model, in order to obtain an accurate recommended index prediction model, proper training data is also required to train the recommended index prediction model.
In this embodiment, historical data of a user recommending an application to be recommended is collected, user data which receives the recommendation of the application to be recommended is obtained as positive sample data which is successfully recommended by the application to be recommended, and user data which does not receive the recommendation of the application to be recommended is obtained as negative sample data which is failed to be recommended by the application to be recommended.
It should be noted that, the user data for recommending the application to be recommended should include the interest degree of the user in the application to be recommended and the intimacy degree between the user and the associated user.
In this way, the positive sample data of successful recommendation of the application to be recommended and the negative sample data of failure recommendation of the application to be recommended are selected respectively to train the recommendation index prediction model, so that the recommendation index prediction model obtained through training can be used for recommending indexes of the application to be recommended for the associated user according to the input intimacy between the target user and the associated user and the interest of the associated user in the application to be recommended.
In another embodiment, after recommending the application to be recommended to the associated user based on the recommendation index of the application to be recommended to the associated user, feedback data of application recommendation initiated by the associated user to the target user may be collected, so as to update the training interestingness prediction model and the recommendation index prediction model according to the feedback data, thereby improving the subsequent recommendation effect on the application to be recommended or other applications.
For example, user data of an application to be recommended, which is used by an associated user to accept a recommendation of a target user, may be used as sample data of the interestingness prediction model for updating the training interestingness prediction model. The affinity between the target user and the associated user in combination with the interest level of the associated user in the application to be recommended may be used as sample data of the recommendation index prediction model to update the training recommendation index prediction model.
The method provided by the application will be described in detail below in a specific application scenario.
As shown in fig. 9, in an exemplary embodiment, the application to be recommended is a game application, if the target user triggers the game application to jump into a game recommendation page or triggers a recommendation button in the game recommendation page during the game application using process, the target user is considered to trigger the recommendation of the game application, at this time, the intimacy between the target user and the WeChat friends thereof is obtained, the characteristic data of the WeChat user is input into a trained interestingness prediction model to predict the interestingness of the WeChat friends thereof to the game application, and the intimacy between the target user and the WeChat friends thereof and the interestingness of the WeChat friends to the game application are input into a trained recommendation index prediction model to predict the recommendation index of the game application to the WeChat friends. After the recommendation indexes of the WeChat friends of the target user are obtained, displaying the WeChat friends with the top five ranks as candidate recommended users to the target user according to the ranking of the recommendation indexes, so that the target user can select the displayed WeChat friends. After the target user selects the WeChat friends which want to be recommended, the target user invites the WeChat friends to experience the game application by sending WeChat messages to the WeChat friends selected by the target user.
After the experience invitation of the game application is initiated to the WeChat friends selected by the target user, the feedback data of the WeChat friends for the game application recommendation are collected, and the training interest degree prediction model and the recommendation index prediction model are updated, so that the updated and trained interest degree prediction model and recommendation index prediction model are used for subsequent application recommendation, and the recommendation effect of the subsequent application recommendation is improved.
Referring to fig. 10, fig. 10 is a block diagram illustrating an application recommendation apparatus 500 according to an exemplary embodiment. As shown in fig. 10, in an exemplary embodiment, the application recommendation apparatus 500 may include an affinity acquisition module 510, an interestingness acquisition module 530, a recommendation index acquisition module 550, and an application recommendation module 570.
The affinity obtaining module 510 is configured to obtain, for a target user of an application to be recommended, an affinity between the target user and an associated user thereof.
The interestingness obtaining module 530 is configured to determine, according to the feature data of the associated user, an interestingness of the associated user for the application to be recommended.
The recommendation index obtaining module 550 is configured to determine a recommendation index of an application to be recommended for an associated user according to the affinity between the target user and the associated user and the interest degree of the associated user for the application to be recommended.
The application recommendation module 570 is configured to recommend an application to be recommended to an associated user based on a recommendation index of the application to be recommended for the associated user.
In another exemplary embodiment, the affinity acquisition module 510 includes an evaluation value acquisition unit and an affinity synthesis acquisition unit.
The evaluation value acquisition unit is used for acquiring evaluation values of each social dimension of the target user and the associated user in the appointed social application, wherein each social dimension of the appointed social application comprises at least one of a receiving and transmitting dimension of instant messaging messages, an interaction dimension on a content sharing platform provided by the appointed social application, an article reading dimension and an application program using dimension.
The affinity comprehensive acquisition unit is used for calculating the affinity between the target user and the associated user based on the weight of each social dimension and the evaluation value of the target user and the associated user in each social dimension.
In another exemplary embodiment, the interestingness acquisition module 530 includes a feature data collection unit and an interestingness prediction model prediction unit.
The feature data collection unit is used for collecting feature data of the associated user, wherein the feature data comprises attribute information of the associated user and usage data of other applications by the associated user
The interestingness prediction model prediction unit is used for inputting the characteristic data into a pre-trained interestingness prediction model to obtain the interestingness of the associated user for the application to be recommended, which is predicted by the interestingness prediction model.
In another exemplary embodiment, the recommendation index obtaining module 550 is configured to input, as feature data, the affinity between the target user and the associated user and the interest of the associated user in the application to be recommended into a pre-trained recommendation index prediction model, and obtain a recommendation index predicted by the recommendation index prediction model for the associated user.
In another exemplary embodiment, the application recommendation apparatus 500 further includes an interestingness sample data acquisition module and an interestingness prediction model training module.
The interestingness sample data acquisition module is used for generating positive sample data according to user data interested in the application to be recommended, and generating negative sample data according to user data not interested in the application to be recommended.
The interestingness prediction model training module is used for training the interestingness prediction model according to the positive sample data and the negative sample data.
In another exemplary embodiment, the application recommendation apparatus 500 further includes an application recommendation sample data acquisition module and a recommendation index prediction model training module.
The application recommendation sample data acquisition module is used for generating positive sample data according to user data of successful recommendation of the application to be recommended, and generating negative sample data according to user data of failure in recommendation of the application to be recommended, wherein the user data comprises the interestingness of the user to the application to be recommended and the intimacy between the user and the associated user.
The recommendation index prediction model training module is used for training the recommendation index prediction model according to the positive sample data and the negative sample data.
In another exemplary embodiment, the application recommendation module 570 is configured to present the associated user according to the recommendation index ranking of the associated user, recommend the associated user selected by the target user to the application to be recommended, or recommend the associated user with the specified ranking according to the recommendation index ranking of the associated user to the application to be recommended.
It should be noted that, the apparatus provided in the foregoing embodiments and the method provided in the foregoing embodiments belong to the same concept, and the specific manner in which each module and unit perform the operation has been described in detail in the method embodiments, which is not repeated herein.
In another exemplary embodiment, the present application also provides a terminal device, including a processor and a memory, where the memory stores computer readable instructions that, when executed by the processor, implement an application recommendation method as described above.
In another exemplary embodiment, the application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the application recommendation method as described above. The computer-readable storage medium may be contained in the terminal device described in the above embodiment or may exist alone without being incorporated in the terminal device.
The foregoing is merely illustrative of the preferred embodiments of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be defined by the claims.
Claims (10)
1. An application recommendation method, the method comprising:
monitoring whether a target user triggers the recommendation of an application to be recommended, and if so, acquiring the intimacy between the target user and an associated user thereof;
according to the feature data of the associated user, determining the interest degree of the associated user for the application to be recommended, wherein the feature data comprises attribute information of the associated user and use data of the associated user for other applications;
Inputting the intimacy between the target user and the associated user and the interest of the associated user for the application to be recommended into a pre-trained recommendation index prediction model as characteristic data, and acquiring a recommendation index of the application to be recommended for the associated user;
recommending the application to be recommended to the associated user based on the recommendation index of the application to be recommended to the associated user.
2. The method of claim 1, wherein the associated user is a friend user of the target user on a designated social application, and the obtaining the affinity between the target user and the associated user comprises:
acquiring evaluation values of the target user and the associated user in each social dimension of the designated social application;
and calculating the intimacy between the target user and the associated user based on the weight of each social dimension and the evaluation value of the target user and the associated user in each social dimension.
3. The method of claim 2, wherein the respective social dimension comprises at least one of:
the method comprises the steps of receiving and transmitting an instant messaging message, and providing an interaction dimension, an article reading dimension and an application program using dimension on a content sharing platform provided by the appointed social application.
4. The method of claim 3, wherein the obtaining the evaluation values of the target user and the associated user in the respective social dimensions of the specified social application comprises:
calculating an evaluation value of the transceiving dimension of the instant communication message according to the number of messages sent by the target user to the associated user and the number of messages sent by the associated user to the target user under the condition that the social dimension comprises the transceiving dimension of the instant communication message;
under the condition that the social dimension comprises an interaction dimension on a content sharing platform provided by the appointed social application, calculating an evaluation value corresponding to the interaction dimension according to the quantity of content sharing initiated by the target user and the quantity of content sharing initiated by the associated user participating in the target user;
calculating an evaluation value of the article reading dimension according to the number of articles read by the target user and the number of articles read by the associated user and the target user together when the social dimension comprises the article reading dimension;
in the case that the social dimension includes an application usage dimension, calculating an evaluation value of the application usage dimension according to the number of applications used by the target user and the number of applications used by the associated user together with the target user.
5. The method of claim 1, wherein the determining the interest level of the associated user in the application to be recommended according to the feature data of the associated user comprises:
collecting characteristic data of the associated user;
and inputting the characteristic data into a pre-trained interestingness prediction model to obtain the interestingness of the associated user for the application to be recommended, which is predicted by the interestingness prediction model.
6. The method of claim 5, wherein the method further comprises:
generating positive sample data according to user data interested in the application to be recommended, and generating negative sample data according to user data not interested in the application to be recommended;
and training the interestingness prediction model according to the positive sample data and the negative sample data.
7. The method of claim 1, wherein the recommending the application to be recommended to the associated user based on a recommendation index of the application to be recommended for the associated user comprises: selecting the associated users with the appointed ranking as candidate users to display to the target users according to the recommendation index ranking of the associated users;
And triggering the selected candidate users to recommend the application to be recommended to the target user.
8. An application recommendation device, the device comprising:
the affinity acquisition module is used for monitoring whether a target user triggers the recommendation of the application to be recommended, and if so, acquiring the affinity between the target user and the associated user;
the interest degree acquisition module is used for determining the interest degree of the associated user for the application to be recommended according to the characteristic data of the associated user, wherein the characteristic data comprises attribute information of the associated user and use data of the associated user for other applications;
the recommendation index obtaining module is used for inputting the intimacy between the target user and the associated user and the interest degree of the associated user for the application to be recommended into a pre-trained recommendation index prediction model as characteristic data to obtain a recommendation index of the application to be recommended for the associated user;
and the application recommendation module is used for recommending the application to be recommended to the associated user based on the recommendation index of the application to be recommended for the associated user.
9. A terminal device comprising a processor and a memory, the memory having stored thereon computer readable instructions which, when executed by the processor, implement the application recommendation method according to any of claims 1-7.
10. A computer readable storage medium having stored thereon computer readable instructions, which when executed by a processor of a computer, cause the computer to perform the application recommendation method according to any of claims 1-7.
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