CN114926234A - Article information pushing method and device, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses an article information pushing method and device, electronic equipment and a computer readable medium. One embodiment of the method comprises: generating a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users to obtain a user attribute vector set and an interest item vector set; clustering the user attribute vector set to generate a user attribute vector cluster group; selecting a user attribute vector cluster corresponding to a target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster; selecting an interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector to obtain an alternative interest item vector group; and generating a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group. This embodiment improves the accuracy of the items pushed for the new user.
Description
Technical Field
The embodiment of the disclosure relates to the field of computers, in particular to an article information pushing method, an article information pushing device, electronic equipment and a computer readable medium.
Background
Item application platforms often recommend items that may be of interest to a user through user history data. At present, an article application platform recommends an article of interest for a new user, and generally adopts the following modes: and determining the user type of the new user according to the business rules, and determining each article corresponding to the user type as an article which is possibly interested by the new user.
However, the following technical problems generally exist in the above manner:
firstly, due to the fact that the business rule is set artificially, the goods pushed for the new user are inaccurate, and time for the new user to browse the goods information is wasted;
secondly, the association relationship between the new user and the historical user of the article application platform is not considered, so that the pushed article cannot meet the browsing requirement of the new user, and the browsing time of the new user is wasted.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose an item information pushing method, apparatus, electronic device and computer readable medium to solve one or more of the technical problems mentioned in the above background section.
In a first aspect, some embodiments of the present disclosure provide an item information pushing method, including: generating a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users to obtain a user attribute vector set and an interest item vector set, wherein the user item association point diagram indicates that an association relationship exists between the behavior users and the items; clustering the user attribute vector set to generate a user attribute vector cluster group; selecting a user attribute vector cluster corresponding to a target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster, wherein the target user attribute vector is as follows: a user attribute vector corresponding to the target non-behavioral user; selecting an interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector to obtain an alternative interest item vector group; and generating a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group, and pushing each item information corresponding to the target interest item vector to a target terminal.
In a second aspect, some embodiments of the present disclosure provide an article information pushing device, including: the system comprises a first generating unit, a second generating unit and a third generating unit, wherein the first generating unit is configured to generate a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users, and obtain a user attribute vector set and an interest item vector set, wherein the user item association point diagram indicates that the behavior users and the items have association relations; the clustering unit is configured to perform clustering processing on the user attribute vector set to generate a user attribute vector cluster group; a first selecting unit, configured to select a user attribute vector cluster corresponding to a target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster, where the target user attribute vector is: a user attribute vector corresponding to the target non-behavioral user; a second selecting unit, configured to select an interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector, so as to obtain an alternative interest item vector group; and the second generating unit is configured to generate a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group, and push each item information corresponding to the target interest item vector to a target terminal.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or at least one processor; a storage device, on which one or at least one program is stored, which when executed by one or at least one processor causes the one or at least one processor to implement the method described in any one of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: according to the article information pushing method of some embodiments of the disclosure, the accuracy of the article pushed for the new user is improved, and the waste of time for the new user to browse the article information is avoided. Specifically, the reason why the time for the new user to browse the item information is wasted is that: due to the fact that the business rules are set manually, the goods pushed for the new user are inaccurate, and time for the new user to browse the goods information is wasted. Based on this, according to the item information pushing method of some embodiments of the present disclosure, first, according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users, a user attribute vector and an interest item vector are generated, and a user attribute vector set and an interest item vector set are obtained. Therefore, by generating the user attribute vector set and the interest item vector set, at least one behavior user similar to the user attribute information corresponding to the new user (target non-behavior user) can be determined conveniently in the follow-up process. Secondly, clustering the user attribute vector set to generate a user attribute vector cluster group. Therefore, each historical user (behavior user) can be classified, the behavior users with similar user attribute information are classified into one class, and the at least one behavior user can be conveniently determined in the follow-up process. And then, selecting a user attribute vector cluster corresponding to the target user attribute vector from the user attribute vector cluster group as the target user attribute vector cluster. Wherein, the target user attribute vector is: and the user attribute vector corresponding to the target non-behavior user. Thus, individual behavioral users similar to the target non-behavioral user can be selected. And then, selecting the interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector to obtain an alternative interest item vector group. Therefore, the interested article vector of each behavior user similar to the target non-behavior user can be determined, and the information of the article interested by the target non-behavior user can be conveniently determined. And finally, generating a target interest item vector corresponding to the target non-behavior user according to the candidate interest item vector group, and pushing each item information corresponding to the target interest item vector to a target terminal. Therefore, the target interest item vector of the target non-behavioral user can be determined by using the interest item vectors of the behavioral users similar to the target non-behavioral user. Therefore, the pushed article information is more accurate, the accuracy of the articles pushed for the new user is improved, and the waste of time for the new user to browse the article information is avoided.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a flow diagram of some embodiments of an item information push method according to the present disclosure;
fig. 2 is a schematic diagram of a user item association point diagram in the item information pushing method according to the present disclosure;
fig. 3 is a schematic structural diagram of some embodiments of an item information pushing device according to the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "at least one" in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or at least one" is contemplated unless the context clearly dictates otherwise.
The names of messages or information exchanged between at least one device in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flow diagram of some embodiments of an item information push method according to some embodiments of the present disclosure. A flow 100 of some embodiments of an item information push method according to the present disclosure is shown. The item information pushing method comprises the following steps:
In some implementations, an executing subject (e.g., a computing device) of the item information pushing method may generate a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user of a plurality of behavior users, so as to obtain a user attribute vector set and an interest item vector set. The user and article association point diagram shows that the association relationship exists between the behavior user and the articles. Wherein the behavior user of the plurality of behavior users may be a user browsing the item information on the item application platform. The user attribute vector may be a vector of attribute information characterizing a behavioral user. The attribute information of the behavioral user may include, but is not limited to, at least one of the following user attribute values: height information, age information, academic information, sex information, and regional information. The interest item vector may be a vector characterizing a plurality of items of interest to the behavioral user. The user item association node map may include a plurality of user item association nodes. The plurality of user item association nodes may include a node corresponding to the user attribute vector and at least one node corresponding to the at least one item vector. And the node corresponding to the user attribute vector is connected with at least one node corresponding to the at least one article vector by using a connecting line. The connecting line may represent that there is an association between the user attribute vector and the item vector. As shown in fig. 2, the nodes associated with the plurality of user items may include a node 201 corresponding to the user attribute vector, a node 202 corresponding to the item vector, and a node 203 corresponding to the item vector. The item information corresponding to the node 202 corresponding to the item vector may be a computer. The item information corresponding to the node 203 corresponding to the item vector may be a camera. The node 201 is connected to the nodes 202 and 203 by connecting lines.
In practice, according to the user attribute information group and the user item association point diagram corresponding to each behavior user in the plurality of behavior users, the execution subject may generate a user attribute vector and an interest item vector by:
firstly, normalizing the user attribute information with continuous characteristics in the user attribute information group to generate normalized user attribute information, and obtaining a normalized user attribute information group. Here, the Normalization processing may refer to Batch Normalization (Batch Normalization) processing. The user attribute information of the continuous feature may be user attribute information including user attribute values of continuous numerical values. For example, the user attribute information of the continuous feature may be height information of the behavioral user.
And secondly, splicing the normalized user attribute information group and the target user attribute information group to generate a spliced user attribute message. The target user attribute information group is each user attribute information with discrete characteristics in the user attribute information group. The user attribute information of the discrete feature may be user attribute information including a user attribute value of a discrete numerical value. For example, the user attribute information of the discrete features may be gender information of the user.
And thirdly, carrying out vector coding processing on the splicing user attribute information to generate a splicing user attribute information vector as a user attribute vector. In practice, the execution body may perform vector encoding processing on the splicing user attribute information through a vector encoding model to generate a splicing user attribute information vector as a user attribute vector. Here, the vector coding model may be a Bert coding model.
And fourthly, inputting the user item association point diagram into a pre-trained neural network to obtain an interest item vector. Here, the Graph neural Network may be a Graph neural Network (GCN).
And 102, clustering the user attribute vector set to generate a user attribute vector cluster group.
In some embodiments, the execution entity may perform a clustering process on the set of user attribute vectors by using a k-means clustering algorithm (k-means clustering algorithm) to generate a user attribute vector cluster group. The number of vector clusters corresponding to the k-means clustering algorithm may be preset. Each user attribute vector in the user attribute vector cluster is relatively similar.
In some optional implementation manners of some embodiments, the executing entity may perform clustering processing on the user attribute vector set to generate a user attribute vector cluster group by:
the first step is to determine the user attribute set associated with the user attribute vector in the user attribute vector set. Each user attribute characterized by the user attribute vector in the user attribute vector set may be determined as a user attribute set. The user attribute vector is generated based on attribute information of each user attribute in the user attribute set. For example, the user attribute may be height information, age information, or the like.
And secondly, determining the number of the user attributes included in the user attribute set as the number of the user attributes.
And thirdly, determining the number of each behavior user included by the behavior users as the number of the behavior users.
And fourthly, determining the cluster number according to the user attribute number and the behavior user number. First, the product of the number of user attributes and the number of behavior users may be determined as the total number of user attributes. Then, the total number of the user attributes may be subjected to root number opening processing, so that the total number of the user attributes subjected to root number opening is used as the cluster number.
And fifthly, clustering the user attribute vector set according to the number of the clusters to generate a user attribute vector cluster group.
In practice, according to the number of clusters, the executing entity may perform clustering processing on the user attribute vector set to generate a user attribute vector cluster group by the following sub-steps:
a first sub-step of randomly selecting a cluster number of user attribute vectors from the set of user attribute vectors as an initial cluster number of cluster center vectors.
And a second sub-step of determining the distance between each user attribute vector and the central vectors of the clusters with the initial cluster number.
And a third substep, dividing each user attribute vector into initial user attribute vector clusters corresponding to cluster centers closest to the cluster center vectors with the number of the initial clusters to obtain initial user attribute vector cluster sets.
And a fourth substep of determining a cluster center vector corresponding to each initial user attribute vector cluster in the initial user attribute vector cluster set. The cluster center vector is an average vector corresponding to each user attribute vector in the initial user attribute vector cluster.
And a fifth substep of determining whether a cluster center vector corresponding to each initial user attribute vector cluster in the initial user attribute vector cluster set is changed. That is, whether the cluster center vector corresponding to the initial user attribute vector cluster is the same as the initially determined cluster center vector corresponding to the initial user attribute vector cluster.
A sixth substep, in response to determining that no change has occurred, determining the initial set of user attribute vectors as a set of user attribute vectors clusters.
And 103, selecting a user attribute vector cluster corresponding to the target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster.
In some embodiments, the executing entity may select a user attribute vector cluster corresponding to the target user attribute vector from the user attribute vector cluster group as the target user attribute vector cluster. Wherein, the target user attribute vector is: and the user attribute vector corresponding to the target non-behavior user. The target non-behavior user is a non-behavior user of the corresponding interest item vector to be determined. The non-behavioral user may be a new user in the item application platform, or a new user viewing an item in the item application platform for the first time.
In practice, the executing entity may select a user attribute vector cluster corresponding to the target user attribute vector from the user attribute vector cluster group as the target user attribute vector cluster by:
firstly, determining a cluster center vector of each user attribute vector cluster in the user attribute vector cluster group to obtain a cluster center vector group.
And secondly, determining the vector distance between each cluster center vector in the cluster center vector group and the target user attribute vector to obtain a vector distance group. In practice, the executing entity may determine a vector distance between each cluster center vector in the cluster center vector group and the target user attribute vector through a vector distance formula, so as to obtain a vector distance group. Here, the vector distance formula may be an euclidean distance formula.
And thirdly, determining the minimum vector distance in the vector distance group as a target vector distance.
And fourthly, determining the cluster center vector corresponding to the target vector distance as a target cluster center vector.
And fifthly, determining the user attribute vector cluster corresponding to the target cluster center vector as a target user attribute vector cluster.
The related content in the step 103 is optional and serves as an invention point of the present disclosure, thereby solving a technical problem mentioned in the background art that "association relationship between the new user and the historical user of the article application platform is not considered, which causes that the pushed article cannot meet the browsing requirement of the new user, and causes waste of browsing time of the new user. ". Factors that contribute to the waste of browsing time for new users are often as follows: the association relationship between the new user and the historical user of the article application platform is not considered, so that the pushed article cannot meet the browsing requirement of the new user, and the browsing time of the new user is wasted. If the above factors are solved, the effect of reducing the waste of the browsing time of the new user can be achieved. Firstly, determining a cluster center vector of each user attribute vector cluster in the user attribute vector cluster group to obtain a cluster center vector group. Thus, data support is provided for subsequent selection of behavioural users similar to the new user (target non-behavioural user). And secondly, determining the vector distance between each cluster center vector in the cluster center vector group and the target user attribute vector to obtain a vector distance group. Thereby, it is convenient to select a behavioural user similar to the new user (target non-behavioural user). Next, the minimum vector distance in the vector distance group is determined as the target vector distance. And then, determining the cluster center vector corresponding to the target vector distance as a target cluster center vector. From this, a behavioral user most similar to the new user (target non-behavioral user) can be determined. And finally, determining the user attribute vector cluster corresponding to the target cluster center vector as a target user attribute vector cluster. Therefore, the interested articles of the new user can be conveniently determined by utilizing the interested articles of the behavior user similar to the new user (the target non-behavior user). Therefore, the subsequently pushed articles can meet the browsing requirements of the new user conveniently, and the waste of the browsing time of the new user is reduced.
And 104, selecting the interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector to obtain an alternative interest item vector group.
In some embodiments, the execution subject may select, from the interest item vector set, an interest item vector corresponding to the target user attribute vector cluster as a candidate interest item vector, to obtain a candidate interest item vector group. In practice, the interest item vector corresponding to each target user attribute vector in the target user attribute vector cluster may be used as a candidate interest item vector to obtain a candidate interest item vector group.
And 105, generating a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group, and pushing each item information corresponding to the target interest item vector to a target terminal.
In some embodiments, the marketing executive body may generate a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group, and push information of each item corresponding to the target interest item vector to the target terminal. Here, the target terminal may refer to a terminal that has logged in an account of the target non-behavioral user.
In practice, the executing subject may determine an average value of the candidate interest item vectors included in the candidate interest item vector group as the target interest item vector.
In practice, the executing agent may push each item information corresponding to the target interest item vector to the target terminal through the following steps:
and step one, generating article information of each interest article corresponding to the target non-behavioral user according to the target interest article vector. In practice, the item information represented by the target interest item vector may be determined as the item information of each interest item corresponding to the target non-behavioral user. In practice, the execution subject may further input the target interested article vector to a decoding network to obtain article information of each interested article. The decoding Network may be a CNN (Convolutional Neural Network).
And secondly, pushing the item information of each interested item to the target terminal.
The above embodiments of the present disclosure have the following advantages: by the article information pushing method of some embodiments of the disclosure, the accuracy of the article pushed for the new user is improved, and the waste of time for the new user to browse the article information is avoided. Specifically, the reason why the time for the new user to browse the item information is wasted is that: due to the fact that the business rules are set manually, the goods pushed for the new user are inaccurate, and time for the new user to browse the goods information is wasted. Based on this, in the item information pushing method according to some embodiments of the present disclosure, first, according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users, a user attribute vector and an interest item vector are generated, and a user attribute vector set and an interest item vector set are obtained. Therefore, by generating the user attribute vector set and the interest item vector set, at least one behavior user similar to the user attribute information corresponding to the new user (target non-behavior user) can be determined conveniently in the follow-up process. Secondly, clustering the user attribute vector set to generate a user attribute vector cluster group. Therefore, each historical user (behavior user) can be classified, behavior users with similar user attribute information are classified into one class, and the at least one behavior user can be conveniently determined subsequently. And then, selecting a user attribute vector cluster corresponding to the target user attribute vector from the user attribute vector cluster group as the target user attribute vector cluster. Wherein, the target user attribute vector is: and the user attribute vector corresponding to the target non-behavior user. Thus, individual behavioral users similar to the target non-behavioral user can be selected. And then, selecting the interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector to obtain an alternative interest item vector group. Therefore, the interested article vector of each behavior user similar to the target non-behavior user can be determined, and the information of the article interested by the target non-behavior user can be conveniently determined. And finally, generating a target interest item vector corresponding to the target non-behavior user according to the candidate interest item vector group, and pushing each item information corresponding to the target interest item vector to a target terminal. Therefore, the target interest item vector of the target non-behavioral user can be determined by using the interest item vectors of the behavioral users similar to the target non-behavioral user. Therefore, the pushed article information is more accurate, the accuracy of the article pushed for the new user is improved, and the waste of time for the new user to browse the article information is avoided.
With further reference to fig. 3, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an article information pushing device, which correspond to those of the method embodiments shown in fig. 1, and which can be applied in various electronic devices.
As shown in fig. 3, the item information pushing apparatus 300 of some embodiments includes: a first generating unit 301, a clustering unit 302, a first selecting unit 303, a second selecting unit 304, and a second generating unit 305. The first generating unit 301 is configured to generate a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users, to obtain a user attribute vector set and an interest item vector set, where the user item association point diagram indicates that there is an association relationship between the behavior user and an item; a clustering unit 302 configured to perform clustering processing on the user attribute vector set to generate a user attribute vector cluster group; a first selecting unit 303, configured to select a user attribute vector cluster corresponding to a target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster, where the target user attribute vector is: a user attribute vector corresponding to the target non-behavioral user; a second selecting unit 304, configured to select an interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector, so as to obtain an alternative interest item vector group; a second generating unit 305, configured to generate a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group, and push each item information corresponding to the target interest item vector to a target terminal.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Referring now to fig. 4, a block diagram of an electronic device 400 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 408 including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or at least one device as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing apparatus 401, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: generating a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users to obtain a user attribute vector set and an interest item vector set, wherein the user item association point diagram indicates that an association relationship exists between the behavior users and the items; clustering the user attribute vector set to generate a user attribute vector cluster group; selecting a user attribute vector cluster corresponding to a target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster, wherein the target user attribute vector is as follows: a user attribute vector corresponding to the target non-behavioral user; selecting an interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector to obtain an alternative interest item vector group; and generating a target interest item vector corresponding to the target non-behavior user according to the candidate interest item vector group, and pushing each item information corresponding to the target interest item vector to a target terminal.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, which may be described as: a processor includes a first generating unit, a clustering unit, a first selecting unit, a second selecting unit, and a second generating unit. Here, the names of these units do not constitute a limitation on the unit itself in some cases, and for example, the first transmission unit may also be described as "a unit that transmits the question-answer reply message to the teacher terminal in response to detection of the first selection operation acting on the submission control".
The functions described herein above may be performed at least in part by one or at least one hardware logic component. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (7)
1. An item information pushing method comprises the following steps:
generating a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users to obtain a user attribute vector set and an interest item vector set, wherein the user item association point diagram indicates that an association relationship exists between the behavior users and the items;
clustering the user attribute vector set to generate a user attribute vector cluster group;
selecting a user attribute vector cluster corresponding to a target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster, wherein the target user attribute vector is as follows: a user attribute vector corresponding to the target non-behavioral user;
selecting an interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector to obtain an alternative interest item vector group;
and generating a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group, and pushing each item information corresponding to the target interest item vector to a target terminal.
2. The method of claim 1, wherein generating a user attribute vector and an interest item vector according to the user attribute information group and the user item association point diagram corresponding to each of the plurality of behavior users comprises:
and inputting the user item association point diagram into a pre-trained graph neural network to obtain an interest item vector.
3. The method of claim 1, wherein the generating a target item of interest vector for the target non-behavioral user from the set of candidate item of interest vectors comprises:
and determining the average value of all candidate interest item vectors included in the candidate interest item vector group as a target interest item vector.
4. The method according to claim 1, wherein the pushing of the item information corresponding to the target interest item vector to the target terminal includes:
generating item information of each interest item corresponding to the target non-behavior user according to the target interest item vector;
and pushing the item information of each interested item to the target terminal.
5. An article information pushing device comprises:
the system comprises a first generating unit, a second generating unit and a third generating unit, wherein the first generating unit is configured to generate a user attribute vector and an interest item vector according to a user attribute information group and a user item association point diagram corresponding to each behavior user in a plurality of behavior users, and obtain a user attribute vector set and an interest item vector set, wherein the user item association point diagram indicates that the behavior users and the items have association relation;
a clustering unit configured to perform clustering processing on the user attribute vector set to generate a user attribute vector cluster group;
a first selecting unit configured to select a user attribute vector cluster corresponding to a target user attribute vector from the user attribute vector cluster group as a target user attribute vector cluster, wherein the target user attribute vector is: a user attribute vector corresponding to the target non-behavioral user;
a second selecting unit, configured to select an interest item vector corresponding to the target user attribute vector cluster from the interest item vector set as an alternative interest item vector, so as to obtain an alternative interest item vector group;
and the second generating unit is configured to generate a target interest item vector corresponding to the target non-behavioral user according to the candidate interest item vector group, and push each item information corresponding to the target interest item vector to a target terminal.
6. An electronic device, comprising:
one or at least one processor;
a storage device having one or more programs stored thereon;
the or at least one program, when executed by the or at least one processor, causes the or at least one processor to carry out the method of any one of claims 1-4.
7. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-4.
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