CN112256969B - Media material pushing method and device, electronic equipment and storage medium - Google Patents

Media material pushing method and device, electronic equipment and storage medium Download PDF

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CN112256969B
CN112256969B CN202011171038.7A CN202011171038A CN112256969B CN 112256969 B CN112256969 B CN 112256969B CN 202011171038 A CN202011171038 A CN 202011171038A CN 112256969 B CN112256969 B CN 112256969B
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materials
media material
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CN112256969A (en
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董琦
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Douyin Vision Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/43—Querying
    • G06F16/435—Filtering based on additional data, e.g. user or group profiles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/45—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/55—Push-based network services

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  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Computer Networks & Wireless Communication (AREA)
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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Electrically Operated Instructional Devices (AREA)

Abstract

The disclosure provides a pushing method, a device, an electronic device and a storage medium of media materials, wherein the pushing method comprises the following steps: selecting a first media material meeting a preset condition based on user behavior data corresponding to the pushed media material; selecting target media materials matched with the first media materials from candidate media materials to be pushed based on user operation characteristics corresponding to the first media materials and second media materials in a media material library; and pushing the target media material.

Description

Media material pushing method and device, electronic equipment and storage medium
Technical Field
The disclosure relates to the technical field of computers, and in particular relates to a media material pushing method, a media material pushing device, electronic equipment and a storage medium.
Background
With the development of internet technology, various applications (apps) are gradually developed, through which a large amount of media materials can be pushed to users, for example, for the media applications, graphic media materials, video media materials and the like can be promoted to users.
When media materials are promoted to users, the latest media materials are pushed to the users according to the release time of the media materials in general cases; or the personalized media materials are directly pushed to the user according to the preference of the user. Media material pushed in the manner described above may not be attractive to users, resulting in a lower effective utilization of the media material.
Disclosure of Invention
The embodiment of the disclosure at least provides a pushing scheme of media materials, so that the effective utilization rate of the pushed media materials is improved.
In a first aspect, an embodiment of the present disclosure provides a method for pushing media materials, including:
selecting a first media material meeting a preset condition based on user behavior data corresponding to the pushed media material;
Selecting target media materials matched with the first media materials from candidate media materials to be pushed based on user operation characteristics corresponding to the first media materials and second media materials in a media material library;
and pushing the target media material.
In one possible implementation manner, the pushed media material is media material of which the release source pushed by other application programs except the target application program is the target application program;
The selecting, based on user operation features corresponding to the first media material and each second media material in the media material library, a target media material matched with the first media material from candidate media materials to be pushed, includes:
Selecting target media materials matched with the first media materials from candidate media materials to be pushed corresponding to the target application program based on the user operation characteristics corresponding to the first media materials and the second media materials when the client associated with the target application program is displayed;
The pushing the target media material includes:
And pushing the target media material through other application programs except the target application program.
In a possible implementation manner, before the selecting, from the candidate media materials to be pushed, the target media material matched with the first media material, the pushing method further includes:
determining a target time period for selecting the candidate media materials based on the release time information corresponding to the first media materials;
and selecting the candidate media materials from the media material library based on the corresponding user operation characteristics of each second media material in the target time period.
In a possible implementation manner, the selecting, from the candidate media materials to be pushed, a target media material matched with the first media material includes:
according to a weight matrix of a pre-trained neural network, respectively determining a first media material vector corresponding to the first media material and candidate media material vectors corresponding to candidate media materials; the weight matrix of the pre-trained neural network is trained based on the user operation characteristics, the first media material and the candidate media material;
And determining target media materials matched with the first media materials based on the first media material vector and candidate media material vectors corresponding to the candidate media materials.
In a possible implementation manner, the determining, based on the first media material vector and the candidate media material vectors corresponding to the candidate media materials, the target media material matched with the first media material includes:
Determining the similarity between the first media material and each candidate media material based on the first media material vector and the candidate media material vector corresponding to each candidate media material;
According to the similarity between the first media material and each candidate media material, the candidate media materials corresponding to the first media material are ordered in a descending order;
and selecting candidate media materials with the similarity exceeding a preset similarity threshold value and the number set before sorting as the target media materials.
In one possible implementation, the weight matrix of the neural network is trained based on the user operational characteristics, the first media material, and the candidate media material in the following manner:
Forming a sample material library based on the first media material and the candidate media material, wherein the sample material library comprises a plurality of sample materials;
based on user operation characteristics corresponding to each sample material, positive sample materials and negative sample materials contained in the sample material library are determined;
Training the neural network based on the positive sample material and the negative sample material respectively, and obtaining a weight matrix corresponding to the neural network after the neural network reaches a preset convergence condition.
In a possible implementation manner, the determining positive sample materials and negative sample materials contained in the sample material library based on the user operation features corresponding to the sample materials includes:
If different sample materials have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of positive sample materials;
If different sample materials do not have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of negative sample materials.
In one possible implementation manner, the user operation features include a plurality of types, and the determining positive sample materials and negative sample materials included in the sample material library based on the user operation features corresponding to the sample materials includes:
If different sample materials are operated by the same user, extracting feature vectors formed by the same user aiming at user operation features of all the sample materials in the different sample materials;
determining whether the similarity of the user operation features of the same user for each sample material in the different sample materials is greater than a set similarity threshold based on feature vectors formed by the user operation features respectively corresponding to the same user;
and if the similarity of the user operation characteristics of the same user for each sample material in the different sample materials is greater than the set similarity threshold, determining that the different sample materials form positive sample materials, otherwise, determining that the different sample materials form negative sample materials.
In a second aspect, an embodiment of the present disclosure provides a pushing device for media materials, including:
The first selection module is used for selecting a first media material meeting a preset condition based on user behavior data corresponding to the pushed media material;
The second selection module is used for selecting target media materials matched with the first media materials from candidate media materials to be pushed based on user operation characteristics corresponding to the first media materials and each second media material in the media material library;
And the material pushing module is used for pushing the target media material.
In one possible implementation manner, the pushed media material is media material of which the release source pushed by other application programs except the target application program is the target application program;
The second selecting module is configured to, when selecting a target media material matched with the first media material from candidate media materials to be pushed based on user operation features corresponding to the first media material and each second media material in a media material library, include:
Selecting target media materials matched with the first media materials from candidate media materials to be pushed corresponding to the target application program based on the user operation characteristics corresponding to the first media materials and the second media materials when the client associated with the target application program is displayed;
the material pushing module, when used for pushing the target media material, comprises:
And pushing the target media material through other application programs except the target application program.
In a possible implementation manner, the second selecting module is further configured to, before selecting, from the candidate media materials to be pushed, a target media material that matches the first media material:
determining a target time period for selecting the candidate media materials based on the release time information corresponding to the first media materials;
and selecting the candidate media materials from the media material library based on the corresponding user operation characteristics of each second media material in the target time period.
In a possible implementation manner, the second selecting module, when used for selecting a target media material matched with the first media material from candidate media materials to be pushed, includes:
according to a weight matrix of a pre-trained neural network, respectively determining a first media material vector corresponding to the first media material and candidate media material vectors corresponding to candidate media materials; the weight matrix of the pre-trained neural network is trained based on the user operation characteristics, the first media material and the candidate media material;
And determining target media materials matched with the first media materials aiming at the first media material vector and candidate media material vectors corresponding to the candidate media materials.
In one possible implementation manner, the second selecting module, when determining, based on the first media material and candidate media material vectors corresponding to the candidate media materials, a target media material that matches the first media material, includes:
Determining the similarity between the first media material and each candidate media material based on the first media material vector and the candidate media material vector corresponding to each candidate media material;
According to the similarity between the first media material and each candidate media material, the candidate media materials corresponding to the first media material are ordered in a descending order;
and selecting candidate media materials with the similarity exceeding a preset similarity threshold value and the number set before sorting as the target media materials.
In one possible implementation, the pushing device further includes a network training module for training the weight matrix of the neural network based on the user operational characteristics, the first media material, and the candidate media material in the following manner:
Forming a sample material library based on the first media material and the candidate media material, wherein the sample material library comprises a plurality of sample materials;
based on user operation characteristics corresponding to each sample material, positive sample materials and negative sample materials contained in the sample material library are determined;
Training the neural network based on the positive sample material and the negative sample material respectively, and obtaining a weight matrix corresponding to the neural network after the neural network reaches a preset convergence condition.
In a possible implementation manner, when the network training module is configured to determine positive sample materials and negative sample materials included in the sample material library based on user operation features corresponding to each sample material, the network training module includes:
If different sample materials have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of positive sample materials;
If different sample materials do not have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of negative sample materials.
In one possible implementation manner, the user operation features include multiple types, and the network training module when used for determining positive sample materials and negative sample materials included in the sample material library based on the user operation features corresponding to the sample materials includes:
If different sample materials are operated by the same user, extracting feature vectors formed by the same user aiming at user operation features of all the sample materials in the different sample materials;
determining whether the similarity of the user operation features of the same user for each sample material in the different sample materials is greater than a set similarity threshold based on feature vectors formed by the user operation features respectively corresponding to the same user;
and if the similarity of the user operation characteristics of the same user for each sample material in the different sample materials is greater than the set similarity threshold, determining that the different sample materials form positive sample materials, otherwise, determining that the different sample materials form negative sample materials.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the push method as described in the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the push method according to the first aspect.
According to the media material pushing method provided by the embodiment of the disclosure, first media materials meeting preset conditions are selected according to user behavior data corresponding to pushed media materials, for example, first media materials with higher effective utilization rate can be selected, further based on user operation characteristics corresponding to second media materials in the first media materials and a media material library, target media materials matched with the first media materials are selected from candidate media materials to be pushed, and because the media materials liked by the same user have certain similarity, target media materials with similar characteristics to the first media materials can be selected from the candidate media materials in such a way, so that the effective utilization rate of the pushed target media materials can be improved when the target media materials are pushed.
The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for the embodiments are briefly described below, which are incorporated in and constitute a part of the specification, these drawings showing embodiments consistent with the present disclosure and together with the description serve to illustrate the technical solutions of the present disclosure. It is to be understood that the following drawings illustrate only certain embodiments of the present disclosure and are therefore not to be considered limiting of its scope, for the person of ordinary skill in the art may admit to other equally relevant drawings without inventive effort.
Fig. 1 shows a flowchart of a method for pushing media materials according to an embodiment of the disclosure;
FIG. 2 illustrates a flow chart of a method of determining target media material provided by an embodiment of the present disclosure;
FIG. 3 illustrates a flow chart of a method for specifically determining target media material provided by embodiments of the present disclosure;
FIG. 4 illustrates a flow chart of a method of determining a weight matrix for a neural network provided by an embodiment of the present disclosure;
FIG. 5 illustrates a network architecture diagram of a neural network provided by an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a pushing device for media materials according to an embodiment of the disclosure;
Fig. 7 shows a schematic diagram of an electronic device provided by an embodiment of the disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, but not all embodiments. The components of the embodiments of the present disclosure, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure provided in the accompanying drawings is not intended to limit the scope of the disclosure, as claimed, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be made by those skilled in the art based on the embodiments of this disclosure without making any inventive effort, are intended to be within the scope of this disclosure.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, may mean including any one or more elements selected from the group consisting of A, B and C.
With the development of internet technology, various application programs are gradually developed, and a great amount of media materials can be pushed to users through the application programs, so that when the media materials are pushed to the users, how to select media materials attracting the users from the great amount of candidate media materials is needed to be solved, and therefore the effective utilization rate of media resources is improved.
Based on the above-mentioned study, the method for pushing media materials provided in the embodiments of the present disclosure first selects, according to user behavior data corresponding to the media materials that have been pushed, a first media material that meets a preset condition, for example, a first media material that has a higher effective utilization rate may be selected, and further selects, based on user operation features corresponding to each second media material in the first media material and the media material library, a target media material that matches the first media material from candidate media materials to be pushed, because the media materials that are liked by the same user have a certain similarity, so that, in this way, a target media material that has a similar feature to the first media material may be selected from the candidate media materials, thereby, when the target media material is pushed, the effective utilization rate of the pushed target media material may be improved.
For the sake of understanding the present embodiment, first, a detailed description will be given of a media material pushing method disclosed in the present embodiment, where an execution body of the media material pushing method provided in the present embodiment is generally a computer device with a certain computing capability, where the computer device includes, for example: a terminal device or server or other processing device. In some possible implementations, the pushing method of the media material may be implemented by a processor invoking computer readable instructions stored in a memory.
Referring to fig. 1, a flowchart of a pushing method of media materials according to an embodiment of the disclosure is shown, where the pushing method includes the following steps S101 to S103:
S101, selecting a first media material meeting preset conditions based on user behavior data corresponding to the pushed media material.
Illustratively, the media material may be published by the target application and then pushed to other applications than the target application for users of other applications to browse and download the target application, e.g., the target application is media application a and the other applications are other media applications than media application a.
In consideration of different time periods, media materials favored by users are different, in order to select a first media material which satisfies a preset condition in the latest time period, the first media material which satisfies the preset condition can be selected from the media materials which are pushed in the set historical time period, for example, the first media material is selected from the media materials which are pushed in the latest half year.
The user behavior data corresponding to the pushed media material is user behavior data authorized by the user, and specifically may include behavior data such as clicking of the media material by the user when the media material is displayed by other application programs, downloading of the target application program after clicking of the media material, and the like.
By counting the user behavior data corresponding to each media material in the set time period, at least one of the click rate corresponding to the media material, the downloading amount of the target application program triggered by the media material and the conversion rate triggered by the media material can be determined, wherein the conversion rate is the ratio of the number of times the target application program is triggered to be downloaded after the media material is clicked to the number of times the media material is clicked, then the first media material can be selected based on one or more of the click rate, the downloading amount and the conversion rate, for example, the media material with the click rate greater than the threshold of the click rate is selected as the first media material, or the media material with the downloading amount greater than the threshold of the downloading amount is selected as the first media material, or the media material with the conversion rate greater than the threshold of the conversion rate is selected as the first media material, and the first media material popular in the user group can be selected according to the mode.
S102, selecting target media materials matched with the first media materials from candidate media materials to be pushed based on user operation characteristics corresponding to the first media materials and second media materials in a media material library.
For example, taking the foregoing pushed media material as the media material pushed by another application program other than the target application program as the target application program, when selecting, from the candidate media materials to be pushed, the target media material matched with the first media material based on the user operation characteristics corresponding to the first media material and each second media material in the media material library, includes:
And selecting target media materials matched with the first media materials from candidate media materials to be pushed corresponding to the target application program based on user operation characteristics corresponding to the first media materials and the second media materials when the client associated with the target application program is displayed.
The target application may be, for example, an application to be promoted, such as a news-like application, and the target application may be the news-like application.
The media material of which the publishing source is the target application refers to the media material stored and published by the server corresponding to the target application.
The media material library may include a mass of second media materials pre-stored in a server corresponding to the target application program, where the second media materials or the first media materials may correspond to user operation features of at least one user when the client associated with the target application program is displayed, where the user operation features of the user with respect to the media materials may include operation features of tag likes, collections, comments, forwarding, sharing, downloading, and the like of the user with respect to the media materials, and the user operation features are user operation features authorized by the user.
Considering that the media material library contains a large amount of second media materials, before selecting the target media materials matched with the first media materials, candidate media materials to be pushed can be selected, for example, candidate media materials to be pushed corresponding to the target application program can be screened out, so that the range of selecting the target media materials can be narrowed, and the efficiency of determining the target media materials can be improved.
For example, when selecting the candidate media materials in the media material library, the second media materials with higher operation times may be selected as the candidate media materials based on the user operation features corresponding to the second media materials, for example, the second media materials liked by more than a set number of user marks may be selected as the candidate media materials, the second media materials clicked and collected by more than a set number of users may be selected as the candidate media materials, or the second media materials meeting the set conditions may also be selected as the candidate media materials through one or more other user operation features, which is not limited in detail herein.
Here, when the target media material is selected, the target media material is selected through the user operation characteristics corresponding to the first media material and each second media material in the media material library, for example, when the first media material is the media material liked by young females, the target media material matched with the first media material selected through the user operation characteristics is liked by young females, and when pushing is performed, the target media material is liked by young females with high probability.
S103, pushing the target media material.
After the target media material is obtained, the target media material may be pushed by other application programs other than the target application program, for example, the target application program is a video type application program, the target media material is video a, video B and video C played by the video type application program, the video a, video B and video C may be played by other application programs, and when a user of the other application program clicks on the video a, video B or video C to watch, the target media material may be skipped to the video type application program and played by the video type application program, so that the user experiences the video type application program.
According to the media material pushing method provided by the embodiment of the disclosure, first media materials meeting preset conditions are selected according to user behavior data corresponding to pushed media materials, for example, first media materials with higher effective utilization rate can be selected, further based on user operation characteristics corresponding to second media materials in the first media materials and a media material library, target media materials matched with the first media materials are selected from candidate media materials to be pushed, and because the media materials liked by the same user have certain similarity, target media materials with similar characteristics to the first media materials can be selected from the candidate media materials in such a way, so that the effective utilization rate of the pushed target media materials can be improved when the target media materials are pushed.
For S101, when selecting the first media material that meets the preset condition, the method may include;
Selecting at least one media material meeting a preset condition from the download amount of the media material triggering target application program, the click rate of the media material and the conversion rate of the media material triggering from the pushed media materials as a first media material meeting the preset condition; the conversion rate is the ratio of the number of times the target application program is triggered to be downloaded after the media material is clicked to the number of times the media material is clicked.
In order to explain the selection process of the first media material, a return result corresponding to the media material may be introduced, for example, the return result corresponding to the pushed media material may include at least one of a click rate corresponding to the piece of media material, a downloading amount triggered by the piece of media material for the target application program, and a conversion rate triggered by the piece of media material, and when the first media material meeting the preset condition is selected, the media material with the return result meeting the preset condition may be selected as the first media material, for example, the first media material with at least one of a downloading amount of the target application program, the click rate of the media material, and the conversion rate triggered by the media material meeting the preset condition in the return result is selected.
The click rate of the media material can be determined by the ratio of the click times and the display times of the media material.
For example, when the return result corresponding to the media material includes the download amount for the target application program, the click rate for the media material, and the conversion rate triggered by the media material, the return result satisfies at least one or more of the following conditions:
(1) The downloading amount is larger than a preset downloading amount threshold value, the clicking rate is larger than a preset clicking rate threshold value, and the conversion rate is larger than a preset conversion rate threshold value;
(2) And after the downloading amount, the click rate and the conversion rate are weighted and summed, the obtained weighted and summed result is larger than a preset score.
Illustratively, assuming that the preset download amount threshold is m, the preset click rate threshold is n, the conversion rate is k, and the preset score is t; taking the first case as an example, if a piece of media material is pushed by other application programs, the corresponding reporting result is that the downloading amount is greater than m, the click rate is greater than n, and the conversion rate is greater than k, the media material can be used as the first media material; taking the second case as an example, carrying out weighted summation on the downloading amount, the click rate and the conversion rate corresponding to one piece of media material, and then obtaining a weighted summation result which is larger than t, wherein the media material can be used as a first media material; when the downloading amount, the clicking rate and the conversion rate are weighted and summed, various corresponding weights of the downloading amount, the clicking rate and the conversion rate can be set on the basis of big data statistics in advance; taking the first case and the second case as examples, if one media material is pushed by other application programs, the corresponding reporting result is that the downloading amount is greater than m, the clicking rate is greater than n, the conversion rate is greater than k, and after weighted summation is performed on the downloading amount, the clicking rate and the conversion rate corresponding to the media material, the obtained weighted summation result is greater than t, the media material can be used as the first media material.
In the embodiment of the disclosure, by selecting the first media material whose return result meets the preset condition, the media material with higher effective utilization rate can be selected from the pushed media materials as the standard for screening other target media materials to be pushed, and based on the standard, the media material with the same effective utilization rate can be screened.
In an implementation manner, before selecting the target media material matched with the first media material from the candidate media materials to be pushed, the pushing method provided by the embodiment of the disclosure further includes:
(1) Determining a target time period for selecting the candidate media materials based on the release time information corresponding to the first media materials;
(2) And selecting candidate media materials from the media material library based on the corresponding user operation characteristics of each second media material in the target time period.
In view of the fact that the media material library contains a large amount of second media materials, in order to quickly extract candidate media materials with comparability to the first media materials from the large amount of second media material library, that is, make the user operation characteristics corresponding to the candidate media materials and the user operation characteristics corresponding to the first media materials have comparability, the target time period for selecting the candidate media materials can be determined based on the release time information of the first media materials in the target application program, specifically, for the release time information corresponding to the first media materials, the time period between the earliest release time and the latest release time of the first media materials can be used as the target time period herein, for example, the first media materials comprise 3 pieces, namely, the first media materials a, the first media materials B and the first media materials C, the release time in the target application program of the first media materials a is 2020, the release time of the first media materials B in the target application program is 2020, the release time of the first media materials C in 2020, and the release time of the first media materials C in 2020, the release time of the first media materials in 2020, and the release time of the first media materials C in 2020, and the release time of the first media materials can be 10 and take the first media materials as the target time period of 2020 and 10 and the first time period of the first time and 2020.
After determining the target time period, candidate media materials can be selected from the media material library based on the user operation characteristics corresponding to each second media material in the media material library in the target time period, specifically, for the situation that the user operation characteristics mentioned above comprise consumption characteristics such as like marking, collection, comment, forwarding, sharing and downloading, when selecting the candidate media materials, at least one second media material with the user operation characteristics meeting the preset condition can be selected as the candidate media materials, for example, the second media materials with the number of like marking in the target time period exceeding the set number in the media material library can be selected as the candidate media materials.
After selecting the candidate media material from the media material library, when selecting the target media material matched with the first media material from the candidate media materials to be pushed, as shown in fig. 2, the following S201 to S202 may be included:
S201, respectively determining a first media material vector corresponding to a first media material and candidate media material vectors corresponding to candidate media materials according to a weight matrix of a pre-trained neural network; the weight matrix of the pre-trained neural network is obtained by training based on user operation characteristics, the first media material and the candidate media materials;
S202, determining target media materials matched with the first media materials based on the first media material vectors and candidate media material vectors corresponding to the candidate media materials.
The weight matrix may map the first media material to a corresponding first media material vector, and map the candidate media material to a corresponding candidate media material vector, and specifically, a neural network capable of converting the media material into the media material vector may be trained through the user operation feature, the first media material and the candidate media material, and a corresponding weight matrix of the neural network is obtained after the training is finished, and a process of training the neural network to obtain the weight matrix will be described later.
According to the weight matrix of the pre-trained neural network, the first media material or the candidate media material can be converted into corresponding media material vectors, and the similarity between the first media material and the candidate media material can be obtained through calculation of the first media material vector and the candidate media material vector, for example, the cosine similarity between the two media material vectors can be calculated, and the similarity between the first media material and the candidate media material can be represented through the cosine similarity.
In the embodiment of the disclosure, the target media material matched with the first media material is selected from the candidate media materials, so that the target media material with the same effective utilization rate as the first media material can be obtained.
Specifically, for the above S202, when determining the target media material matched with the first media material based on the first media material vector and the candidate media material vector corresponding to each candidate media material, as shown in fig. 3, the following S301 to S303 may be included:
S301, determining the similarity between the first media material and each candidate media material based on the first media material vector and the candidate media material vector corresponding to each candidate media material;
S302, sorting the candidate media materials corresponding to the first media materials in a descending order according to the similarity between the first media materials and each candidate media material;
S303, selecting candidate media materials with the similarity exceeding a preset similarity threshold value and the number set before sorting as target media materials.
When determining the target media material matched with the first media material, the similarity between the first media material vector corresponding to the first media material and the candidate media material vector corresponding to each candidate media material can be determined according to a cosine similarity formula, so that the similarity between the first media material and each candidate media material can be obtained, for example, when the candidate media material contains 1000 pieces of first media material, 1000 pieces of similarity corresponding to the first media material can be obtained.
For 1000 similarities corresponding to the first media material, the 1000 candidate media materials may be sorted in descending order according to a manner of decreasing the similarity, further, the similarity between the 1000 candidate media materials and the first media material may be selected to exceed a preset similarity threshold, and the number of candidate media materials set before sorting are used as target media materials matched with the first media material.
In the case where the first media material includes a plurality of pieces, the target media material that matches each of the first media materials can be obtained in the same manner.
Specifically, for the weight matrix of the neural network mentioned above, as shown in fig. 4, the neural network may be trained based on the user operation feature, the first media material, and the candidate media material to obtain the weight matrix in the following manner, which specifically includes the following S401 to S403:
S401, forming a sample material library based on the first media material and the candidate media material, wherein the sample material library comprises a plurality of sample materials;
s402, positive sample materials and negative sample materials contained in a sample material library are determined based on user operation characteristics corresponding to the sample materials;
s403, training the neural network based on the positive sample material and the negative sample material respectively, and obtaining a weight matrix corresponding to the neural network after the neural network reaches a preset convergence condition.
When the neural network is trained to obtain the weight matrix, the first media material and the candidate media material can be formed into a sample material library after the first media material and the candidate media material are obtained according to the determination method of the first media material and the candidate media material mentioned in the disclosure, and each first media material or candidate media material is used as a sample material in the sample material library.
Before training the neural network, the sample materials in the sample material library are classified, for example, the sample materials can be divided into a plurality of groups of positive sample materials and a plurality of groups of negative sample materials according to user operation characteristics corresponding to the sample materials, and each group of positive sample materials and each group of negative sample materials comprise two sample materials.
In one embodiment, determining positive sample materials and negative sample materials included in the sample material library based on user operation features corresponding to each sample material may include:
(1) If different sample materials have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of positive sample materials;
(2) If different sample materials do not have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of negative sample materials.
For example, the above determination may be performed in a manner of every two sample materials when determining the positive sample materials and the negative sample materials, resulting in a plurality of sets of positive sample materials and a plurality of sets of negative sample materials.
The preset user operation feature may be a specific one or more user operation features, for example, taking the user operation feature "mark like" mentioned above as an example, a sample material that is marked like by the same user may be used as a positive sample material, and a sample material that is not marked like by the same user may be marked as a negative sample material, so that the positive sample material and the negative sample material contained in the sample material library may be obtained.
In another embodiment, the user operation features include multiple types, and when determining positive sample materials and negative sample materials included in the sample material library based on the user operation features corresponding to each sample material, the method may include:
(1) If different sample materials are operated by the same user, extracting feature vectors formed by the same user aiming at user operation features of all the sample materials in the different sample materials;
(2) Determining whether the similarity of the user operation features of the same user for each sample material in different sample materials is greater than a set similarity threshold value based on feature vectors formed by the user operation features respectively corresponding to the same user;
(3) If the similarity of the user operation characteristics of the same user for each sample material in different sample materials is larger than a set similarity threshold, determining that the different sample materials form positive sample materials, otherwise, determining that the different sample materials form negative sample materials.
For example, for two sample materials that have been "Zhang Sano" operated by the same user, they are recorded as sample material A and sample material B, respectively, if the user operation features include: the user operation characteristics of the user 'Zhang San' on the sample material A comprise like, collection and comment, a characteristic vector formed by the user 'Zhang San' on the user operation characteristics of the sample material A can be expressed as [ 111 000 ], the user 'Zhang San' on the user operation characteristics of the sample material B comprise like, collection, comment and forwarding, and a characteristic vector formed by the user 'Zhang San' on the user operation characteristics of the sample material B can be expressed as [ 111 100 ].
Further, the similarity between the sample material a and the sample material B can be determined by the feature vectors [ 111 000 ] and [ 1111 00 ] corresponding to the user 'Zhang san', and whether the similarity is larger than a set similarity threshold value, for example, the similarity threshold value is set to be 0.8, then it can be determined whether the cosine similarity between the feature vector [ 111 000 ] and the feature vector [ 1111 00 ] is larger than 0.8, if so, the sample material a and the sample material B form positive sample materials, otherwise, the sample material a and the sample material B form negative sample materials.
In the embodiment of the disclosure, the positive sample material and the negative sample material contained in the sample material library can be judged according to whether the positive sample material and the negative sample material are operated by the same user, and based on the positive sample material and the negative sample material, other media materials which are liked by a crowd who likes the first media material can be found, so that the target media material with the same effective utilization rate as the first media material is obtained.
In particular, the number of negative sample materials determined in the above two ways may be far greater than the number of positive sample materials, so that after the negative sample materials are obtained, negative sample materials close to the number of positive sample materials or negative sample materials slightly greater than the number of positive sample materials can be sampled to meet the requirement of neural network training.
After the positive sample material and the negative sample material in the sample material library are obtained in the above manner, the neural network may be trained based on the positive sample material and the negative sample material, in the training process, the embodiment of the disclosure may perform initial encoding on each sample material in the sample material library based on the thought of word2vec, according to the mode of item2vec, to obtain one-hot encoding corresponding to each sample material, for example, the sample material library contains 100 sample materials, the 100 sample materials are ordered and recorded as 1 to 100, then one-hot encoding corresponding to each sample material may be represented as a vector of 100 dimensions, one-hot encoding corresponding to the 1 st sample material may be represented as [1 0..0 ], that is, by using a vector of which the first feature value is 1 and the rest feature values are all 0, one-hot encoding corresponding to the 2 nd sample material may be represented as [ 0.0..0 ], that is, by using a vector of which the rest feature values are all 0, and the one-hot encoding corresponding to each sample material in the sample material library may be obtained according to the mode.
Specifically, in the training process, a Skip-Gram network model structure can be introduced, as shown in fig. 5, sample material x and sample material y1, sample material y2 and sample material y3 respectively form a group of positive sample materials, the sample material x and sample material y1, sample material y2 and sample material y3 can be input into a network model as shown in fig. 5, in the training process of the neural network, one-hot coding of the sample material x can be mapped to a feature vector through a weight matrix W, as shown in fig. 5, [ h 1 ... hi ... hN-1 hN ], then based on the feature vector and a weight matrix W ', a predictive coding vector corresponding to the sample material y1, sample material y2 and sample material y3 respectively is obtained, then a loss value corresponding to a neural network is determined according to the one-hot coding and predictive coding vector corresponding to the sample material y1, sample material y2 and sample material y3 respectively, the weight matrix W and W ' are continuously adjusted through the loss value, in addition, a negative sample group is introduced to train the neural network, the neural network is mapped to a feature vector according to the weight matrix W, the same as the weight matrix W ' is determined, the threshold value is set, the threshold value is reached, the number of the training condition is reached, the threshold value is reached, the training condition is reached by setting the threshold value is reached by the training process of the positive sample material, and the threshold value is reached, the similarity between the feature vectors determined by the sample materials y1, y2 and y3 through the weight matrix W' is high, so that a plurality of sample materials which all form negative sample materials with the same sample materials are mapped into feature vectors with low similarity.
In the embodiment of the disclosure, it is proposed that the neural network is trained by judging positive sample materials and negative sample materials contained in the sample material library according to user operation characteristics, so as to obtain a weight matrix for mapping the sample materials into feature vectors, and then a target media material similar to the first media material can be determined based on the feature vectors.
It will be appreciated by those skilled in the art that in the above-described method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.
Based on the same technical concept, the embodiments of the present disclosure further provide a pushing device corresponding to a pushing method of media materials, and since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to that of the pushing method in the embodiments of the present disclosure, implementation of the device may refer to implementation of the method, and repeated parts will not be repeated.
Referring to fig. 6, a schematic diagram of a pushing device 600 for media materials according to an embodiment of the disclosure is provided, where the pushing device includes:
a first selection module 601, configured to select a first media material that meets a preset condition based on user behavior data corresponding to the pushed media material;
A second selection module 602, configured to select, based on the first media material and user operation features corresponding to each second media material in the media material library, a target media material that matches the first media material from candidate media materials to be pushed;
the material pushing module 603 is configured to push the target media material.
In one embodiment, the pushed media material is media material with a release source pushed by other application programs except the target application program as the target application program;
The second selection module 602, when configured to select, based on the first media material and the user operation feature corresponding to each second media material in the media material library, a target media material that matches the first media material from the candidate media materials to be pushed, includes:
Selecting target media materials matched with the first media materials from candidate media materials to be pushed corresponding to the target application program based on user operation characteristics corresponding to the first media materials and the second media materials when the client associated with the target application program is displayed;
The material pushing module 603, when configured to push the target media material, includes:
and pushing the target media material by other application programs except the target application program.
In one embodiment, the second selection module 602 is further configured to, before selecting the target media material matching the first media material from the candidate media materials to be pushed:
Determining a target time period for selecting the candidate media materials based on the release time information corresponding to the first media materials;
And selecting candidate media materials from the media material library based on the corresponding user operation characteristics of each second media material in the target time period.
In one embodiment, the second selection module 602, when configured to select a target media material that matches the first media material from the candidate media materials to be pushed, includes:
According to a weight matrix of a pre-trained neural network, respectively determining a first media material vector corresponding to a first media material and candidate media material vectors corresponding to candidate media materials; the weight matrix of the pre-trained neural network is obtained by training based on the user operation characteristics, the first media material and the candidate media materials;
And determining target media materials matched with the first media materials aiming at the first media material vector and candidate media material vectors corresponding to the candidate media materials.
In one embodiment, the second selection module 602, when configured to determine, based on the first media material and the candidate media material vectors corresponding to the candidate media materials, a target media material that matches the first media material, includes:
Determining the similarity between the first media material and each candidate media material based on the first media material vector and the candidate media material vector corresponding to each candidate media material;
According to the similarity between the first media material and each candidate media material, the candidate media materials corresponding to the first media material are ordered in a descending order;
And selecting candidate media materials with the similarity exceeding a preset similarity threshold value and the number set before sorting as target media materials.
In one embodiment, the pushing device further comprises a network training module 604, the network training module 604 being configured to train the weight matrix of the neural network based on the user operational characteristics, the first media material, and the candidate media material in the following manner:
based on the first media material and the candidate media material, a sample material library is formed, and the sample material library comprises a plurality of sample materials;
based on the user operation characteristics corresponding to the sample materials, positive sample materials and negative sample materials contained in a sample material library are determined;
training the neural network based on the positive sample material and the negative sample material respectively, and obtaining a weight matrix corresponding to the neural network after the neural network reaches a preset convergence condition.
In one embodiment, when the network training module 604 is configured to determine positive sample materials and negative sample materials contained in the sample material library based on user operation features corresponding to each sample material, the network training module includes:
If different sample materials have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of positive sample materials;
if different sample materials do not have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of negative sample materials.
In one embodiment, the user operation features include multiple types, and the network training module 604, when configured to determine positive sample materials and negative sample materials included in the sample material library based on the user operation features corresponding to each sample material, includes:
If different sample materials are operated by the same user, extracting feature vectors formed by the same user aiming at user operation features of all the sample materials in the different sample materials;
Determining whether the similarity of the user operation features of the same user for each sample material in different sample materials is greater than a set similarity threshold value based on feature vectors formed by the user operation features respectively corresponding to the same user;
If the similarity of the user operation characteristics of the same user for each sample material in different sample materials is larger than a set similarity threshold, determining that the different sample materials form positive sample materials, otherwise, determining that the different sample materials form negative sample materials.
The process flow of each module in the apparatus and the interaction flow between the modules may be described with reference to the related descriptions in the above method embodiments, which are not described in detail herein.
Corresponding to the pushing method of the media material in fig. 1, the embodiment of the disclosure further provides an electronic device 700, as shown in fig. 7, which is a schematic structural diagram of the electronic device 700 provided in the embodiment of the disclosure, including:
A processor 71, a memory 72, and a bus 73; memory 72 is used to store execution instructions, including memory 721 and external memory 722; the memory 721 is also called an internal memory, and is used for temporarily storing operation data in the processor 71 and data exchanged with an external memory 722 such as a hard disk, the processor 71 exchanges data with the external memory 722 through the memory 721, and when the electronic device 700 is operated, the processor 71 and the memory 72 communicate through the bus 73, so that the processor 71 executes the following instructions: selecting a first media material meeting a preset condition based on user behavior data corresponding to the pushed media material; selecting target media materials matched with the first media materials from candidate media materials to be pushed based on user operation characteristics corresponding to the first media materials and second media materials in a media material library; and pushing the target media material.
The disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the pushing method of media material described in the method embodiments above. Wherein the storage medium may be a volatile or nonvolatile computer readable storage medium.
The computer program product of the media material pushing method provided in the embodiments of the present disclosure includes a computer readable storage medium storing program codes, where the program codes include instructions for executing the steps of the media material pushing method described in the above method embodiments, and specific reference may be made to the above method embodiments, which are not repeated herein.
The disclosed embodiments also provide a computer program which, when executed by a processor, implements any of the methods of the previous embodiments. The computer program product may be realized in particular by means of hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied as a computer storage medium, and in another alternative embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), or the like.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again. In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present disclosure, and are not intended to limit the scope of the disclosure, but the present disclosure is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, it is not limited to the disclosure: any person skilled in the art, within the technical scope of the disclosure of the present disclosure, may modify or easily conceive changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features thereof; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims (10)

1. A method of pushing media material, comprising:
Selecting a first media material meeting a preset condition based on user behavior data corresponding to the pushed media material, wherein the pushed media material is a media material of which a release source pushed by other application programs except a target application program is the target application program;
Selecting target media materials matched with the first media materials from candidate media materials to be pushed corresponding to the target application program based on user operation characteristics corresponding to the first media materials and second media materials in a media material library when a client associated with the target application program is displayed;
And pushing the target media material through other application programs except the target application program.
2. The pushing method according to claim 1, wherein before selecting a target media material matched with the first media material from the candidate media materials to be pushed corresponding to the target application program, the pushing method further includes:
determining a target time period for selecting the candidate media materials based on the release time information corresponding to the first media materials;
and selecting the candidate media materials from the media material library based on the corresponding user operation characteristics of each second media material in the target time period.
3. The pushing method according to claim 1, wherein selecting, from the candidate media materials to be pushed corresponding to the target application, a target media material that matches the first media material includes:
according to a weight matrix of a pre-trained neural network, respectively determining a first media material vector corresponding to the first media material and candidate media material vectors corresponding to candidate media materials; the weight matrix of the pre-trained neural network is trained based on the user operation characteristics, the first media material and the candidate media material;
And determining target media materials matched with the first media materials based on the first media material vector and candidate media material vectors corresponding to the candidate media materials.
4. A pushing method according to claim 3, wherein the determining, based on the first media material vector and candidate media material vectors corresponding to the candidate media materials, a target media material matching the first media material includes:
Determining the similarity between the first media material and each candidate media material based on the first media material vector and the candidate media material vector corresponding to each candidate media material;
According to the similarity between the first media material and each candidate media material, the candidate media materials corresponding to the first media material are ordered in a descending order;
and selecting candidate media materials with the similarity exceeding a preset similarity threshold value and the number set before sorting as the target media materials.
5. A push method as claimed in claim 3, wherein the weight matrix of the neural network is trained based on the user operational characteristics, the first media material and the candidate media material in the following manner:
Forming a sample material library based on the first media material and the candidate media material, wherein the sample material library comprises a plurality of sample materials;
based on user operation characteristics corresponding to each sample material, positive sample materials and negative sample materials contained in the sample material library are determined;
Training the neural network based on the positive sample material and the negative sample material respectively, and obtaining a weight matrix corresponding to the neural network after the neural network reaches a preset convergence condition.
6. The pushing method according to claim 5, wherein the determining positive sample materials and negative sample materials contained in the sample material library based on the user operation features corresponding to each sample material includes:
If different sample materials have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of positive sample materials;
If different sample materials do not have the same preset user operation characteristics aiming at the same user, determining that the different sample materials form a group of negative sample materials.
7. The pushing method according to claim 5, wherein the user operation features include a plurality of types, and the determining positive sample materials and negative sample materials included in the sample material library based on the user operation features corresponding to the sample materials includes:
If different sample materials are operated by the same user, extracting feature vectors formed by the same user aiming at user operation features of all the sample materials in the different sample materials;
determining whether the similarity of the user operation features of the same user for each sample material in the different sample materials is greater than a set similarity threshold based on feature vectors formed by the user operation features respectively corresponding to the same user;
and if the similarity of the user operation characteristics of the same user for each sample material in the different sample materials is greater than the set similarity threshold, determining that the different sample materials form positive sample materials, otherwise, determining that the different sample materials form negative sample materials.
8. A pushing device for media material, comprising:
The first selection module is used for selecting a first media material meeting preset conditions based on user behavior data corresponding to the pushed media material, wherein the pushed media material is a media material of which a release source pushed by other application programs except a target application program is the target application program;
The second selection module is used for selecting target media materials matched with the first media materials from candidate media materials to be pushed corresponding to the target application program based on user operation characteristics corresponding to the first media materials and second media materials in a media material library when the client associated with the target application program is displayed;
And the material pushing module is used for pushing the target media material through other application programs except the target application program.
9. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating over the bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the push method according to any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when run by a processor, performs the steps of the pushing method according to any of claims 1 to 7.
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