CN113626679B - Multimedia resource recommendation method, device and storage medium - Google Patents
Multimedia resource recommendation method, device and storage medium Download PDFInfo
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
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Abstract
The disclosure discloses a multimedia resource recommendation method, a device and a storage medium, which relate to the technical field of mobile internet and at least solve the problem of low accuracy of multimedia resource recommendation in the related technology. In the method, a first prediction parameter set and a second prediction parameter set of candidate multimedia resources are obtained by predicting the interactive operation of the candidate multimedia resources by a target account; and determining screening parameters according to the acquired first prediction parameter set and the second prediction parameter set, and recommending the multimedia resources to the target account according to the screening parameters. In this way, more accurate recommendation results are generated according to the screening parameters obtained by the first prediction parameter set and the second prediction parameter set, and the accuracy of recommending the multimedia resources to the target account is further improved.
Description
Technical Field
The disclosure relates to the technical field of mobile internet, and in particular relates to a multimedia resource recommendation method, a device and a storage medium.
Background
With the development of the age, electronic devices have become a part of life, and in order to meet the use demands of users, various application software is generally downloaded and installed in the electronic devices. The user may watch video, listen to music, etc. through the application software. In order to recommend multimedia resources such as videos, music or news and the like which are interested in the user to the user, the application software screens the multimedia resources which are interested in the user from a resource library through a recommendation algorithm.
However, existing recommendation algorithms place a center of gravity on the act of focusing on the publisher of the recommended multimedia asset after the user views the multimedia asset. Ignoring the generated attention does not necessarily lead to a valid state change, e.g. the user is watching the publisher but no longer watching other multimedia resources of the publisher. Therefore, the existing recommendation method has no way to distinguish which behaviors belong to the behaviors effectively causing the state change, so that the accuracy of recommending the multimedia resources is reduced.
Disclosure of Invention
The embodiment of the disclosure provides a multimedia resource recommendation method, a device and a storage medium, so as to improve the accuracy of recommending multimedia resources to users.
According to a first aspect of an embodiment of the present disclosure, there is provided a multimedia resource recommendation method, including:
acquiring a candidate multimedia resource set of a target account; wherein the candidate multimedia resource set comprises at least two candidate multimedia resources for recommending to the target account;
generating a first prediction parameter set and a second prediction parameter set corresponding to the candidate multimedia resources, wherein the first prediction parameter set is used for recording each prediction parameter of the target account for executing interactive operation on the candidate multimedia resources; the second prediction parameter set is used for recording each prediction parameter of the interaction operation of the target account on other multimedia resources released by the release account after the target account and the release account of the candidate multimedia resources establish social relations; the social relationship is not established between the target account and the release account;
Obtaining screening parameters of the candidate multimedia resources based on the first prediction parameter set and the second prediction parameter set;
and screening the multimedia resources from the candidate multimedia resource set according to the screening parameters to recommend the multimedia resources to the target account.
In a possible implementation manner, the obtaining, based on the first prediction parameter set and the second prediction parameter set, a filtering parameter of the candidate multimedia resource includes:
carrying out fusion processing on each prediction parameter in the second prediction parameter set to obtain a correction parameter;
and correcting the predicted parameters in the first predicted parameter set according to the corrected parameters to obtain screening parameters of the candidate multimedia resources.
In a possible implementation manner, the correcting the prediction parameters in the first prediction parameter set according to the correction parameters to obtain the screening parameters of the candidate multimedia resources includes:
multiplying the prediction parameters associated with the correction parameters in the first prediction parameter set by the correction parameters to obtain corrected prediction parameters; the prediction parameter associated with the correction parameter is a prediction parameter of the target account performing a specific operation on the release account through the candidate multimedia resource, wherein the specific operation is one of the interactive operations;
And carrying out weighted summation calculation on the corrected prediction parameters and other prediction parameters in the first prediction parameter set according to preset coefficients to obtain screening parameters of the candidate multimedia resources.
In a possible implementation manner, the correcting the prediction parameters in the first prediction parameter set according to the correction parameters to obtain the screening parameters of the candidate multimedia resources includes:
and carrying out weighted summation calculation on each prediction parameter in the first prediction parameter set and the correction parameter according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
In a possible implementation manner, the correcting the prediction parameters in the first prediction parameter set according to the correction parameters to obtain the screening parameters of the candidate multimedia resources includes:
extracting the characteristics of the correction parameters to obtain correction characteristics; a kind of electronic device with high-pressure air-conditioning system;
extracting features of each prediction parameter in the first prediction parameter set to obtain features of each prediction parameter;
and obtaining the screening parameters of the candidate multimedia resources by carrying out linear calculation on the characteristics of each prediction parameter and the correction characteristics.
In one possible implementation, the second set of prediction parameters corresponding to the candidate multimedia resource is determined by:
extracting the characteristics of the account information of the release account to obtain release account characteristics; a kind of electronic device with high-pressure air-conditioning system;
extracting the characteristics of account information of an associated account set with an associated relation with the release account to obtain associated account characteristics; the associated account set comprises at least two associated accounts, and the account information of the associated account set comprises account information of each associated account and interaction operation executed by each associated account on the multimedia resource of the release account;
and determining the second prediction parameter set of the interactive operation executed by the target account on the release account according to the release account characteristics and the association account characteristics.
In a possible implementation manner, the selecting, according to the selection parameter, a multimedia resource from the candidate multimedia resource set to recommend to the target account includes:
determining multimedia resources to be recommended from the candidate multimedia resource set according to the screening parameters;
if the number of the multimedia resources of the same type in the determined multimedia resources to be recommended is greater than a preset threshold, screening the multimedia resources of the same type, the number of which is greater than the preset threshold, according to the screening parameter; wherein the types of the multimedia resources are obtained by classifying the contents of the multimedia resources;
Recommending the filtered multimedia resources and other multimedia resources to be recommended to the target account.
According to a second aspect of the embodiments of the present disclosure, there is provided a multimedia resource recommendation apparatus, including:
an acquisition unit configured to perform acquisition of a candidate multimedia resource set of a target account; wherein the candidate multimedia resource set comprises at least two candidate multimedia resources for recommending to the target account;
a prediction parameter set determining unit configured to perform generation of a first prediction parameter set and a second prediction parameter set corresponding to the candidate multimedia resource, wherein the first prediction parameter set is used for recording each prediction parameter of the target account for performing interactive operation on the candidate multimedia resource; the second prediction parameter set is used for recording each prediction parameter of the interaction operation of the target account on other multimedia resources released by the release account after the target account and the release account of the candidate multimedia resources establish social relations; the social relationship is not established between the target account and the release account;
a filter parameter determining unit configured to perform a filter parameter obtaining the candidate multimedia resource based on the first prediction parameter set and the second prediction parameter set;
And the recommending unit is configured to execute the screening of the multimedia resources from the candidate multimedia resource set according to the screening parameters to recommend the multimedia resources to the target account.
In one possible implementation, determining the screening parameter element includes:
the fusion subunit is configured to perform fusion processing on each prediction parameter in the second prediction parameter set to obtain a correction parameter;
and the correction subunit is configured to perform correction on the prediction parameters in the first prediction parameter set according to the correction parameters to obtain the screening parameters of the candidate multimedia resources.
In one possible implementation, the correction subunit includes:
a first correction subunit configured to perform multiplication of a prediction parameter associated with the correction parameter in the first prediction parameter set with the correction parameter to obtain a corrected prediction parameter; the prediction parameter associated with the correction parameter is a prediction parameter of the target account performing a specific operation on the release account through the candidate multimedia resource, wherein the specific operation is one of the interactive operations;
and the screening parameter determining subunit is configured to perform weighted summation calculation on the modified prediction parameter and other prediction parameters in the first prediction parameter set according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
In one possible implementation, the correction subunit includes:
and the second correction subunit is configured to perform weighted summation calculation on each prediction parameter in the first prediction parameter set and the correction parameter according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
In one possible implementation, the correction subunit includes:
a first feature extraction subunit configured to perform feature extraction on the correction parameters to obtain correction features;
a second feature extraction subunit configured to perform feature extraction on each prediction parameter in the first prediction parameter set, so as to obtain a feature of each prediction parameter;
and the third correction subunit is configured to perform linear calculation on the characteristics of each prediction parameter and the correction characteristics to obtain the screening parameters of the candidate multimedia resources.
In one possible implementation, the second set of prediction parameters corresponding to the candidate multimedia resource is determined by:
the first feature extraction unit is configured to perform feature extraction on account information of the release account to obtain release account features;
The second feature extraction unit is configured to perform feature extraction on account information of an associated account set with an association relationship with the release account to obtain associated account features; the associated account set comprises at least two associated accounts, and the account information of the associated account set comprises account information of each associated account and interaction operation executed by each associated account on the multimedia resource of the release account;
a second set of prediction parameters unit configured to perform the second set of prediction parameters that determine the interaction performed by the target account on the publishing account according to the publishing account feature and the associated account feature.
In one possible implementation, the recommendation unit includes:
a multimedia resource subunit is determined and configured to determine multimedia resources to be recommended from the candidate multimedia resource set according to the screening parameters;
a screening subunit configured to perform screening on the multimedia resources in the same type, the number of which is greater than the preset threshold, according to the screening parameter if the determined number of the multimedia resources in the same type is greater than the preset threshold; wherein the types of the multimedia resources are obtained by classifying the contents of the multimedia resources;
And the recommending subunit is configured to execute the recommendation of the screened multimedia resources and other multimedia resources to be recommended to the target account.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement a multimedia resource recommendation method;
according to a fourth aspect of embodiments of the present disclosure, there is provided a storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform a multimedia asset recommendation method;
according to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the multimedia asset recommendation method provided by the embodiments of the present disclosure.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
The method comprises the steps that through predicting interactive operation of a target account on candidate multimedia resources, a first prediction parameter set and a second prediction parameter set of the candidate multimedia resources are obtained, wherein the second prediction parameter set is used for recording all prediction parameters of the interactive operation of the target account on other multimedia resources released by the release account after the social relationship between the target account and the release account of the candidate multimedia resources is established; and determining screening parameters according to the acquired first prediction parameter set and the second prediction parameter set, and recommending the multimedia resources to the target account according to the screening parameters. In this way, by acquiring the second prediction parameter set, the prediction parameters of the target account for executing the interactive operation on other multimedia resources of the release account after paying attention to the release account can be predicted, so that a more accurate recommendation result is generated according to the screening parameters obtained by the first prediction parameter set and the second prediction parameter set, and the accuracy of recommending the multimedia resources to the target account is further improved.
Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the disclosure. The objectives and other advantages of the disclosure will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the present disclosure, and together with the description serve to explain the present disclosure. In the drawings:
FIG. 1 is a schematic illustration of an application interface in an embodiment of the present disclosure;
fig. 2 is a flow chart of a multimedia resource recommendation method in an embodiment of the disclosure;
fig. 3 is a schematic structural diagram of a multimedia resource recommendation device according to an embodiment of the disclosure;
fig. 4 is a schematic structural diagram of a terminal device in an embodiment of the disclosure.
Detailed Description
In order to improve accuracy of recommending multimedia resources to users, the embodiment of the disclosure provides a multimedia resource recommending method, a device and a storage medium. In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
The following describes the technical scheme provided by the embodiments of the present disclosure with reference to the accompanying drawings.
In order to improve user viscosity, the existing application software generally provides two pages in the application, one page is a recommended page for recommending multimedia resources to the user, and the other page is a multimedia resource released by an author concerned by the user. Taking video as an example, a user can watch recommended video on a recommended page or watch video published by an author concerned on a concerned page. As shown in fig. 1, which is a schematic diagram of an application interface. Two touch options are respectively recommended and focused above the application interface, and the user enters the corresponding page by clicking the corresponding touch options. Such as: and if the user clicks the recommendation page, the application displays the recommendation page, and the user clicks the video in the recommendation page to play the recommendation video.
The application hopes that after the user browses the favorite video on the recommended page, the user can generate the behavior of focusing on the video author, and further the behavior is converted into focusing on the page to browse the video, so that the viscosity of the user can be improved, the production enthusiasm of the author can be improved, and the whole content ecology forms a good cycle.
However, in the existing recommendation scheme, the action that the user pays attention after browsing the video is taken as a parameter to recommend the video for the user, but the user is neglected to pay attention to the author of the video, and the user may not pay attention to the page to browse the video. For example: after focusing on the author who likes the video, the user finds other videos that do not like the author. Thus, in this case the user may not be interested in the page to view and manipulate the author's video. Therefore, the existing recommendation scheme does not pay attention to the behavior of the user after attention, so that the effective conversion from recommending the page browsing video to the page browsing video is low, and the accuracy of recommending the multimedia resources is reduced.
In view of the above, the present disclosure provides a multimedia resource recommendation method, when recommending a video to a user, not only needs to consider the interaction operations (such as clicking, praying, forwarding, focusing, etc.) of the video after the user browses the video, but also needs to consider the interaction operations (such as clicking, praying, forwarding, etc.) of other videos of the author after the user focuses on the author of the video. In this way, the accuracy of recommending the multimedia resource to the target account is further improved by predicting the first parameter set of the target account to the video and the second parameter set of the target account to other videos of the video author and obtaining the screening parameters according to the first parameter set and the second parameter set to generate a more accurate recommendation result.
For easy understanding, the technical scheme provided by the present disclosure is further described below with reference to the accompanying drawings.
Fig. 2 is a flowchart illustrating a multimedia asset recommendation method according to an exemplary embodiment, as shown in fig. 2, including the following steps.
In step S21, a candidate multimedia resource set of the target account is obtained; the candidate multimedia resource set comprises at least two candidate multimedia resources for recommending to the target account.
Wherein, the multimedia resources comprise multimedia information such as music, video, news and the like.
The candidate multimedia resource set is obtained by roughly sequencing all videos through big data. For example: and screening out the associated account similar to the account information of the target account from the resource library according to the account information (such as the gender, age, region, browsed multimedia resources and the like of the user) of the target account, and selecting a candidate multimedia resource set from the multimedia resources browsed by the associated account.
In step S22, a first prediction parameter set and a second prediction parameter set corresponding to the candidate multimedia resource are generated, where the first prediction parameter set is used to record each prediction parameter of the target account for executing the interactive operation on the candidate multimedia resource; the second prediction parameter set is used for recording each prediction parameter of the interaction operation of the target account on other multimedia resources released by the release account after the target account and the release account of the candidate multimedia resources establish social relations; and the social relationship between the target account and the release account is not established.
In the embodiment of the disclosure, after a candidate multimedia resource set is acquired, a screening parameter of each multimedia resource is determined for each multimedia resource in the set. The screening parameters consist of two parts, namely a first prediction parameter set and a second prediction parameter set.
Wherein the first set of prediction parameters comprises prediction parameters of the target account's interaction with the multimedia asset, such as: click rate, attention rate, praise rate, forwarding rate, and the like. The second prediction parameter set includes prediction parameters of the interaction of the target account with other multimedia resources released by the release account after focusing on the release account of the multimedia resources, for example: click rate, praise rate, and forwarding rate.
In the embodiment of the disclosure, two prediction models may be established to respectively predict the first prediction parameter set and the second prediction parameter set. The first prediction model for predicting the first prediction parameter set takes the multimedia resource as an input object, and outputs and obtains each prediction parameter of the multimedia resource relative to the target account; the second prediction model for predicting the second prediction parameter set takes the release account as an input object, and outputs and obtains each prediction parameter of the target account for other multimedia resources of the release account. How to train the two models is explained further below:
And aiming at the first pre-estimated model, acquiring a multimedia resource sample during training, wherein the multimedia resource sample comprises user side characteristics and content side characteristics. The user side features include a click list (the click list includes account information for clicking the multimedia resource, such as account identification, user gender, age, etc.), a endorsement list (the endorsement list includes account information endorsed by the multimedia resource), a focus list (the focus list includes account information of an issuing account focused by the multimedia resource), and a forwarding list (the forwarding list includes account information for forwarding the multimedia resource). The content-side features include: and issuing account information, click rate, praise rate, attention rate, forwarding rate and other characteristics of the account. And the multimedia resource sample is provided with a label, and the label is information such as whether the multimedia resource sample is clicked, endorsed, focused, forwarded and the like. For example: if the label of the multimedia resource sample is clicked, praise, attention and forwarding are not carried out, the label is a positive sample of clicking, a negative sample of praise, a negative sample of attention and a negative sample of forwarding.
Training the first pre-estimation model according to the multimedia resource sample and the label of the multimedia resource sample, and adjusting parameters in the pre-estimation model to enable the error between the output result and the label to be in a preset range.
And aiming at the second pre-estimated model, acquiring a multimedia resource sample on the concerned page during training. Wherein the multimedia sample includes a user-side feature and an author-side feature. The user side features comprise information such as a clicking list, a praying list, a forwarding list and the like of the multimedia resource issued by the clicking author; the author side characteristics comprise information such as the number of author vermicelli, the activity of the author, the gender of the author, the age and the like.
In the embodiment of the disclosure, operations performed by a user on a recommended page and a concerned page are recorded separately. For example: if the user performs operations such as praise and forwarding on a video, and praise is performed on the recommended page and forwarding is performed on the concerned page, in the recommended page, the user information of the user can only be found in the praise list, but the user cannot be found in the forwarding list.
And the multimedia resource sample is provided with a label which is information such as whether the multimedia resource sample is clicked, endorsed, forwarded and the like. For example: if the label of the multimedia resource sample is clicked, praise, attention and forwarding are not paid, the label is a positive sample of clicking, a negative sample of praise and a negative sample of forwarding.
Training the second pre-estimation model according to the multimedia resource sample and the label of the multimedia resource sample, and adjusting parameters in the pre-estimation model to enable the error between the output result and the label to be in a preset range.
It should be noted that the difference between the first pre-estimation model and the second pre-estimation model is the selection of the training samples and the selection of the features. The first pre-estimated model is a sample selected on a recommended page, and the selected characteristics are user side characteristics and content side characteristics; the second pre-estimated model is a sample selected on the concerned page, and the selected features are user side features and author side features.
After the trained second pre-estimated model is obtained, extracting the characteristics of the account information of the release account to obtain the characteristics of the release account; and extracting the characteristics of the account information of the associated account set with the association relation with the release account to obtain the associated account characteristics.
The associated account set comprises at least two associated accounts, and the account information of the associated account set comprises account information of each associated account and interaction operation executed by each associated account on the multimedia resource of the release account.
And determining the second prediction parameter set of the interactive operation executed by the target account on the release account according to the release account characteristics and the association account characteristics.
In the embodiment of the disclosure, the user-side features and the author-side features are input into a second prediction model, calculated through a matrix, and a second prediction parameter set is obtained through an activation function of a calculation result.
In step S23, a screening parameter of the candidate multimedia resource is obtained based on the first prediction parameter set and the second prediction parameter set.
And obtaining a first prediction parameter set and a second prediction parameter set of each multimedia resource through two prediction models, and obtaining screening parameters of each multimedia resource through processing. The method can be concretely implemented as follows:
and carrying out fusion processing on each prediction parameter in the second prediction parameter set to obtain a correction parameter.
In the embodiment of the disclosure, the correction parameters are obtained by performing weighted summation on the numerical values of the prediction parameters by using preset weighting coefficients.
And correcting the predicted parameters in the first predicted parameter set according to the corrected parameters to obtain screening parameters of the candidate multimedia resources.
In the embodiment of the disclosure, the correction parameters may be used to correct the prediction parameters in the first prediction parameter set by the following three methods.
First kind:
multiplying the prediction parameters associated with the correction parameters in the first prediction parameter set by the correction parameters to obtain corrected prediction parameters; the prediction parameter associated with the correction parameter is a prediction parameter of the target account performing a specific operation on the release account through the candidate multimedia resource, wherein the specific operation is one of the interactive operations;
and carrying out weighted summation calculation on the corrected prediction parameters and other prediction parameters in the first prediction parameter set according to preset coefficients to obtain screening parameters of the candidate multimedia resources.
In the embodiment of the present disclosure, since the correction parameter is a parameter for performing an interactive operation on other multimedia resources of the publishing account after focusing on the publishing account for the target account, when correction is performed, the correction parameter may be used to correct the focusing parameter in the first prediction parameter set. And multiplying the correction parameter with the attention parameter to obtain the attention correction parameter, and then carrying out weighted summation on the attention correction parameter, the click parameter, the praise parameter and the forwarding parameter in the first prediction parameter set by a preset weighting coefficient to obtain the screening parameter. Such as: screening parameter = a click parameter + b praise parameter + c forwarding parameter + d correction parameter, attention parameter. Wherein a, b, c, d are all weighting coefficients.
Second kind:
and carrying out weighted summation calculation on each prediction parameter in the first prediction parameter set and the correction parameter according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
In the embodiment of the disclosure, the correction parameter can be directly used as a prediction parameter to perform weighted summation calculation with other prediction parameters to obtain the screening parameter. Such as: screening parameter = a click parameter + b praise parameter + c forwarding parameter + d correction parameter + e attention parameter. Wherein a, b, c, d, e are all weighting coefficients.
Third kind:
extracting the characteristics of the correction parameters to obtain correction characteristics; extracting features of each prediction parameter in the first prediction parameter set to obtain features of each prediction parameter;
and obtaining the screening parameters of the candidate multimedia resources by carrying out linear calculation on the characteristics of each prediction parameter and the correction characteristics.
In the embodiment of the application, after the correction parameters and the first prediction parameter set are obtained, the prediction parameters are sequenced through a reordering model, so as to obtain the screening parameters.
Therefore, the first prediction parameter set is corrected by the correction parameters obtained by fusing the second prediction parameter set, so that the final screening score also pays attention to other multimedia resources of the release account after the target account pays attention to the release account, the obtained screening score is more comprehensive, and the quality of the multimedia resources recommended according to the screening score is higher.
In step S24, the candidate multimedia resource set is screened for multimedia resources to recommend to the target account according to the screening parameters.
In the embodiment of the disclosure, after the screening parameters of each multimedia resource are obtained, recommending is performed according to the preset number of multimedia resources of the screening parameters.
In one embodiment, a predetermined number of multimedia resources may be selected based on the high to low filter parameters. For example: the candidate multimedia resource library has 10 multimedia resources in total, 5 multimedia resources are selected for recommendation, and the first 5 multimedia resources with the highest value of the screening parameters can be used as multimedia resources to be recommended according to the screening parameters of each multimedia resource.
In one embodiment, a threshold is set for the screening parameter of the multimedia resources, and the multimedia resources with the screening parameter higher than the threshold are used as the multimedia resources to be recommended. For example: the candidate multimedia resource library has 10 multimedia resources in total, the set threshold value is 85, and if the screening parameter is higher than the multimedia resources of 85, the 4 multimedia resources are used as the multimedia resources to be recommended.
Therefore, the quality of recommending the multimedia resources to the target account can be improved, and the retention of the user can be effectively improved by predicting the first parameter set of the target account to the video and the second parameter set of the target account to other videos of the video author and obtaining the screening parameters according to the first parameter set and the second parameter set.
However, when recommending the multimedia resources to the target account, the user viewing experience is poor due to the consistent types of the multimedia resources to be recommended, and in order to solve this problem, further screening of the multimedia resources to be recommended is required, which may be implemented as follows:
determining multimedia resources to be recommended from the candidate multimedia resource set according to the screening parameters;
if the number of the multimedia resources of the same type in the determined multimedia resources to be recommended is greater than a preset threshold, screening the multimedia resources of the same type, the number of which is greater than the preset threshold, according to the screening parameter; wherein the types of the multimedia resources are obtained by classifying the contents of the multimedia resources;
in one embodiment, the preset threshold is set to 4, if the determined to-be-recommended multimedia resources have 10 multimedia resources, and 5 multimedia resources are of the same type, the 5 multimedia resources are further filtered, so as to obtain 4 multimedia resources finally, and the 4 multimedia resources and other 5 multimedia resources are recommended to the target account.
Recommending the filtered multimedia resources and other multimedia resources to be recommended to the target account.
In this way, the viewing experience of the user can be improved by further screening the multimedia resources to be recommended according to the multimedia resource type.
Based on the same inventive concept, the disclosure also provides a multimedia resource recommendation device. Fig. 3 is a schematic diagram of a multimedia resource recommendation device provided in the present disclosure. The device comprises:
an obtaining unit 301 configured to perform obtaining a candidate multimedia resource set of a target account; wherein the candidate multimedia resource set comprises at least two candidate multimedia resources for recommending to the target account;
a prediction parameter set determining unit 302, configured to perform generating a first prediction parameter set and a second prediction parameter set corresponding to the candidate multimedia resource, where the first prediction parameter set is used to record each prediction parameter of the target account for performing an interactive operation on the candidate multimedia resource; the second prediction parameter set is used for recording each prediction parameter of the interaction operation of the target account on other multimedia resources released by the release account after the target account and the release account of the candidate multimedia resources establish social relations; the social relationship is not established between the target account and the release account;
A determine screening parameter unit 303 configured to perform a screening parameter based on the first set of prediction parameters and the second set of prediction parameters, resulting in the candidate multimedia resource;
and a recommending unit 304 configured to execute the process of selecting multimedia resources from the candidate multimedia resource set to recommend to the target account according to the selection parameter.
In one possible implementation, determining the screening parameter unit 303 includes:
the fusion subunit is configured to perform fusion processing on each prediction parameter in the second prediction parameter set to obtain a correction parameter;
and the correction subunit is configured to perform correction on the prediction parameters in the first prediction parameter set according to the correction parameters to obtain the screening parameters of the candidate multimedia resources.
In one possible implementation, the correction subunit includes:
a first correction subunit configured to perform multiplication of a prediction parameter associated with the correction parameter in the first prediction parameter set with the correction parameter to obtain a corrected prediction parameter; the prediction parameter associated with the correction parameter is a prediction parameter of the target account performing a specific operation on the release account through the candidate multimedia resource, wherein the specific operation is one of the interactive operations;
And the screening parameter determining subunit is configured to perform weighted summation calculation on the modified prediction parameter and other prediction parameters in the first prediction parameter set according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
In one possible implementation, the correction subunit includes:
and the second correction subunit is configured to perform weighted summation calculation on each prediction parameter in the first prediction parameter set and the correction parameter according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
In one possible implementation, the correction subunit includes:
a first feature extraction subunit configured to perform feature extraction on the correction parameters to obtain correction features;
a second feature extraction subunit configured to perform feature extraction on each prediction parameter in the first prediction parameter set, so as to obtain a feature of each prediction parameter;
and the third correction subunit is configured to perform linear calculation on the characteristics of each prediction parameter and the correction characteristics to obtain the screening parameters of the candidate multimedia resources.
In one possible implementation, the second set of prediction parameters corresponding to the candidate multimedia resource is determined by:
The first feature extraction unit is configured to perform feature extraction on account information of the release account to obtain release account features;
the second feature extraction unit is configured to perform feature extraction on account information of an associated account set with an association relationship with the release account to obtain associated account features; the associated account set comprises at least two associated accounts, and the account information of the associated account set comprises account information of each associated account and interaction operation executed by each associated account on the multimedia resource of the release account;
a second set of prediction parameters unit configured to perform the second set of prediction parameters that determine the interaction performed by the target account on the publishing account according to the publishing account feature and the associated account feature.
In one possible implementation, the recommendation unit 303 includes:
a multimedia resource subunit is determined and configured to determine multimedia resources to be recommended from the candidate multimedia resource set according to the screening parameters;
a screening subunit configured to perform screening on the multimedia resources in the same type, the number of which is greater than the preset threshold, according to the screening parameter if the determined number of the multimedia resources in the same type is greater than the preset threshold; wherein the types of the multimedia resources are obtained by classifying the contents of the multimedia resources;
And the recommending subunit is configured to execute the recommendation of the screened multimedia resources and other multimedia resources to be recommended to the target account.
As shown in fig. 4, the embodiment of the present disclosure further provides an electronic device 40, which may include a memory 401 and a processor 402, based on the same technical concept.
The memory 401 is used for storing a computer program executed by the processor 402. The memory 401 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the task management device, and the like. The processor 402 may be a central processing unit (central processing unit, CPU), or a digital processing unit, etc. The particular connection medium between the memory 401 and the processor 402 described above is not limited in the embodiments of the present disclosure. The embodiment of the present disclosure is shown in fig. 4, where the memory 401 and the processor 402 are connected by a bus 403, where the bus 403 is shown in fig. 4 with a thick line, and the connection between other components is merely illustrative, and not limited to. The bus 403 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
The memory 401 may be a volatile memory (RAM) such as a random-access memory (RAM); the memory 401 may also be a nonvolatile memory (non-volatile memory), such as a read-only memory, a flash memory (flash memory), a Hard Disk Drive (HDD) or a Solid State Drive (SSD), or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. Memory 401 may be a combination of the above.
A processor 402 for executing the method performed by the device in the embodiment shown in fig. 1 when invoking the computer program stored in said memory 401.
In some possible implementations, aspects of the methods provided by the present disclosure may also be implemented in the form of a program product comprising program code for causing a computer device to carry out the steps of the methods according to the various exemplary embodiments of the disclosure described above when the program product is run on the computer device, e.g. the computer device may carry out the methods as carried out by the apparatus in the examples shown in fig. 1-2.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
While the preferred embodiments of the present disclosure have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (14)
1. A method for recommending multimedia resources, the method comprising:
acquiring a candidate multimedia resource set of a target account; wherein the candidate multimedia resource set comprises at least two candidate multimedia resources for recommending to the target account;
generating a first prediction parameter set and a second prediction parameter set corresponding to the candidate multimedia resources, wherein the first prediction parameter set is used for recording each prediction parameter of the target account for executing interactive operation on the candidate multimedia resources; the second prediction parameter set is used for recording each prediction parameter of the interaction operation of the target account on other multimedia resources released by the release account after the target account and the release account of the candidate multimedia resources establish social relations; the social relationship is not established between the target account and the release account;
Obtaining screening parameters of the candidate multimedia resources based on the first prediction parameter set and the second prediction parameter set;
screening multimedia resources from the candidate multimedia resource set according to the screening parameters to recommend the multimedia resources to the target account;
wherein the obtaining the screening parameter of the candidate multimedia resource based on the first prediction parameter set and the second prediction parameter set includes:
carrying out fusion processing on each prediction parameter in the second prediction parameter set to obtain a correction parameter;
and correcting the predicted parameters in the first predicted parameter set according to the corrected parameters to obtain screening parameters of the candidate multimedia resources.
2. The method of claim 1, wherein the modifying the prediction parameters in the first prediction parameter set according to the modification parameters to obtain the screening parameters of the candidate multimedia resources includes:
multiplying the prediction parameters associated with the correction parameters in the first prediction parameter set by the correction parameters to obtain corrected prediction parameters; the prediction parameter associated with the correction parameter is a prediction parameter of the target account performing a specific operation on the release account through the candidate multimedia resource, wherein the specific operation is one of the interactive operations;
And carrying out weighted summation calculation on the corrected prediction parameters and other prediction parameters in the first prediction parameter set according to preset coefficients to obtain screening parameters of the candidate multimedia resources.
3. The method of claim 1, wherein the modifying the prediction parameters in the first prediction parameter set according to the modification parameters to obtain the screening parameters of the candidate multimedia resources includes:
and carrying out weighted summation calculation on each prediction parameter in the first prediction parameter set and the correction parameter according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
4. The method of claim 1, wherein the modifying the prediction parameters in the first prediction parameter set according to the modification parameters to obtain the screening parameters of the candidate multimedia resources includes:
extracting the characteristics of the correction parameters to obtain correction characteristics; the method comprises the steps of,
extracting features of each prediction parameter in the first prediction parameter set to obtain features of each prediction parameter;
and obtaining the screening parameters of the candidate multimedia resources by carrying out linear calculation on the characteristics of each prediction parameter and the correction characteristics.
5. The method according to claim 1, wherein the second set of prediction parameters corresponding to the candidate multimedia resource is determined by:
extracting the characteristics of the account information of the release account to obtain release account characteristics; the method comprises the steps of,
extracting the characteristics of account information of an associated account set with an associated relation with the release account to obtain associated account characteristics; the associated account set comprises at least two associated accounts, and the account information of the associated account set comprises account information of each associated account and interaction operation executed by each associated account on the multimedia resource of the release account;
and determining the second prediction parameter set of the interactive operation executed by the target account on the release account according to the release account characteristics and the association account characteristics.
6. The method of claim 1, wherein the selecting multimedia resources from the candidate multimedia resource set to recommend to the target account according to the selection parameter comprises:
determining multimedia resources to be recommended from the candidate multimedia resource set according to the screening parameters;
If the number of the multimedia resources of the same type in the determined multimedia resources to be recommended is greater than a preset threshold, screening the multimedia resources of the same type, the number of which is greater than the preset threshold, according to the screening parameter; wherein the types of the multimedia resources are obtained by classifying the contents of the multimedia resources;
recommending the filtered multimedia resources and other multimedia resources to be recommended to the target account.
7. A multimedia asset recommendation device, comprising:
an acquisition unit configured to perform acquisition of a candidate multimedia resource set of a target account; wherein the candidate multimedia resource set comprises at least two candidate multimedia resources for recommending to the target account;
a prediction parameter set determining unit configured to perform generation of a first prediction parameter set and a second prediction parameter set corresponding to the candidate multimedia resource, wherein the first prediction parameter set is used for recording each prediction parameter of the target account for performing interactive operation on the candidate multimedia resource; the second prediction parameter set is used for recording each prediction parameter of the interaction operation of the target account on other multimedia resources released by the release account after the target account and the release account of the candidate multimedia resources establish social relations; the social relationship is not established between the target account and the release account;
A filter parameter determining unit configured to perform a filter parameter obtaining the candidate multimedia resource based on the first prediction parameter set and the second prediction parameter set;
a recommending unit configured to execute a recommendation of screening multimedia resources from the candidate multimedia resource set to the target account according to the screening parameter;
wherein determining the screening parameter unit comprises:
the fusion subunit is configured to perform fusion processing on each prediction parameter in the second prediction parameter set to obtain a correction parameter;
and the correction subunit is configured to perform correction on the prediction parameters in the first prediction parameter set according to the correction parameters to obtain the screening parameters of the candidate multimedia resources.
8. The multimedia asset recommendation device of claim 7, wherein the correction subunit comprises:
a first correction subunit configured to perform multiplication of a prediction parameter associated with the correction parameter in the first prediction parameter set with the correction parameter to obtain a corrected prediction parameter; the prediction parameter associated with the correction parameter is a prediction parameter of the target account performing a specific operation on the release account through the candidate multimedia resource, wherein the specific operation is one of the interactive operations;
And the screening parameter determining subunit is configured to perform weighted summation calculation on the modified prediction parameter and other prediction parameters in the first prediction parameter set according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
9. The multimedia asset recommendation device of claim 7, wherein the correction subunit comprises:
and the second correction subunit is configured to perform weighted summation calculation on each prediction parameter in the first prediction parameter set and the correction parameter according to a preset coefficient to obtain the screening parameter of the candidate multimedia resource.
10. The multimedia asset recommendation device of claim 7, wherein the correction subunit comprises:
a first feature extraction subunit configured to perform feature extraction on the correction parameters to obtain correction features; the method comprises the steps of,
a second feature extraction subunit configured to perform feature extraction on each prediction parameter in the first prediction parameter set, so as to obtain a feature of each prediction parameter;
and the third correction subunit is configured to perform linear calculation on the characteristics of each prediction parameter and the correction characteristics to obtain the screening parameters of the candidate multimedia resources.
11. The multimedia asset recommendation device of claim 7, wherein the second set of prediction parameters corresponding to the candidate multimedia asset is determined by:
the first feature extraction unit is configured to perform feature extraction on account information of the release account to obtain release account features; the method comprises the steps of,
the second feature extraction unit is configured to perform feature extraction on account information of an associated account set with an association relationship with the release account to obtain associated account features; the associated account set comprises at least two associated accounts, and the account information of the associated account set comprises account information of each associated account and interaction operation executed by each associated account on the multimedia resource of the release account;
a second set of prediction parameters unit configured to perform the second set of prediction parameters that determine the interaction performed by the target account on the publishing account according to the publishing account feature and the associated account feature.
12. The multimedia asset recommendation device of claim 7, wherein the recommendation unit comprises:
a multimedia resource subunit is determined and configured to determine multimedia resources to be recommended from the candidate multimedia resource set according to the screening parameters;
A screening subunit configured to perform screening on the multimedia resources in the same type, the number of which is greater than the preset threshold, according to the screening parameter if the determined number of the multimedia resources in the same type is greater than the preset threshold; wherein the types of the multimedia resources are obtained by classifying the contents of the multimedia resources;
and the recommending subunit is configured to execute the recommendation of the screened multimedia resources and other multimedia resources to be recommended to the target account.
13. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the multimedia asset recommendation method of any of claims 1 to 6.
14. A storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the multimedia asset recommendation method of any one of claims 1 to 6.
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