CN114238784A - Content recommendation method, device, system, apparatus, medium, and program product - Google Patents
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Abstract
The disclosure relates to the technical field of computers, and provides a content recommendation method, a content recommendation device, a content recommendation system, an electronic device, a computer-readable storage medium and a computer program product. The method comprises the following steps: receiving model selection operation of a first user, and selecting at least one first recommendation model for displaying; responding to a model merging instruction of a first user, merging at least one first recommendation model corresponding to the model merging instruction to a current recommendation model to generate a first merging model; and in response to the merging completion instruction of the first user, determining target recommended content for the first user based on the obtained first merging model. The method and the device can enable the user to perform model training autonomously and purposefully in combination with the recommendation models of other users so as to perform content recommendation based on the recommendation models obtained through merged training, and can effectively improve the matching degree and accuracy of content recommendation.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a content recommendation method, a content recommendation apparatus, a content recommendation system, an electronic device, a computer-readable storage medium, and a computer program product.
Background
With the rapid development of computer networks, computer networks have been widely used in various fields of people's lives, for example, online shopping, watching videos through networks, browsing news information, and the like have become daily lives of people. As network information content explodes, people increasingly want to mine information content of interest from massive network resources, and therefore it is very important and urgent to accurately mine user preferences and make efficient and accurate content recommendations.
Currently, algorithm recommendation systems can be divided into three categories: collaborative Filtering (Collaborative Filtering) systems, content-based recommendation systems, and hybrid recommendation models; the collaborative filtering system generates recommendations only by using interaction information of users and recommended contents, the content-based recommendation system generates recommendations by using user preferences and/or recommended content preferences, and the hybrid recommendation model generates recommendations by using the interaction information, the users and metadata of the recommended contents. Models in the above categories have corresponding limitations such as data sparsity, cold start of the user and recommended content.
Disclosure of Invention
The present disclosure provides a content recommendation method, a content recommendation apparatus, a content recommendation system, an electronic device, a computer-readable storage medium, and a computer program product, so as to at least solve the problems in the related art that a recommendation system relies too much on user history data to increase the use cost of a user, and that a recommendation model cannot be intervened by the user to cause low accuracy of recommended content and increase user churn. The technical scheme of the disclosure is as follows:
according to a first aspect of the embodiments of the present disclosure, there is provided a content recommendation method, including: receiving model selection operation of a first user, and selecting at least one first recommendation model for displaying; responding to a model merging instruction of the first user, merging at least one first recommendation model corresponding to the model merging instruction to a current recommendation model to generate a first merging model; and responding to a merging completion instruction of the first user, and determining target recommended content for the first user based on the obtained first merging model.
In an exemplary embodiment of the present disclosure, the method further includes: removing the merged recommendation model in the first merging model to generate a second merging model in response to the model removing instruction of the first user; and in response to a removal completion instruction of the first user, determining target recommended content for the first user based on the obtained second merging model.
In an exemplary embodiment of the present disclosure, the method further comprises: the first recommendation model is a recommendation model corresponding to a second user; the second user is a user who opens a model sharing option.
In an exemplary embodiment of the present disclosure, receiving a model selection operation of a first user, and selecting at least one first recommendation model for presentation further includes: receiving model selection operation of the first user based on a model selection area, and determining a model keyword corresponding to the model selection operation; and selecting at least one first recommended model from a model library according to the model keywords and displaying the first recommended model on the interactive interface.
In an exemplary embodiment of the disclosure, determining the target recommended content for the first user based on the obtained first merging model includes: determining a model weight of each recommended model in the first merging model; determining the content acquisition proportion of the first user for each recommendation model according to each model weight; distributing corresponding content request quantity for each recommendation model according to the content acquisition proportion so as to determine the target recommendation content according to the content request quantity.
In an exemplary embodiment of the present disclosure, determining a model weight of each of the recommended models in the first merged model includes: obtaining historical recommendation data of each recommendation model in the first combination model; the historical recommendation data includes a plurality of recommendation influencing factors; determining the factor weight of each recommendation influence factor according to the historical recommendation data; performing a weighted calculation based on a plurality of the factor weights to determine the model weights.
In an exemplary embodiment of the present disclosure, determining a model weight of each of the recommended models in the first merged model includes: determining a content tag of each recommendation model in the first merging model; determining the model priority of each recommendation model according to the content tag; and determining the model weight corresponding to each recommended model according to the model priority.
In an exemplary embodiment of the present disclosure, the method further includes: determining an associated user associated with the first user, generating an associated user group based on the first user and the associated user; responding to the model training operation of any user in the associated user group, and determining associated group recommended content corresponding to the associated user group; and displaying the associated group recommendation content to any user in the associated user group.
According to a second aspect of embodiments of the present disclosure, there is provided a content recommendation system including: the user side is used for providing a model operation interface, displaying target recommended content and receiving model operation performed by a user based on the model operation interface; the model operation comprises a model selection operation, a model removal operation and a model sharing operation; the model training end is used for merging the first recommendation model determined based on the model selection operation into the current recommendation model to generate a first merging model; removing at least one recommended model in the first merging model according to the model removing operation of the first merging model to generate a second merging model; and the content recommendation platform is used for determining target recommendation content for the user according to the merging model and sending the target recommendation content to the user side.
According to a third aspect of the embodiments of the present disclosure, there is provided a content recommendation apparatus including: the model display module is configured to execute model selection operation of a first user and select at least one first recommended model for display; the model merging module is configured to execute a model merging instruction responding to the first user, and merge at least one first recommended model corresponding to the model merging instruction into a current recommended model to generate a first merged model; a first content recommendation module configured to execute a merge completion instruction in response to the first user, and determine a target recommended content for the first user based on the obtained first merge model.
In an exemplary embodiment of the present disclosure, the content recommendation apparatus further includes a second content recommendation module configured to perform: removing the merged recommendation model in the first merging model to generate a second merging model in response to the model removing instruction of the first user; and in response to a removal completion instruction of the first user, determining target recommended content for the first user based on the obtained second merging model.
In an exemplary embodiment of the present disclosure, the model presentation module further comprises a model presentation unit configured to perform: receiving model selection operation of the first user based on a model selection area, and determining a model keyword corresponding to the model selection operation; and selecting at least one first recommended model from a model library according to the model keywords and displaying the first recommended model on the interactive interface.
In an exemplary embodiment of the present disclosure, the first content recommendation module includes a first content recommendation unit configured to perform: determining a model weight of each recommended model in the first merging model; determining the content acquisition proportion of the first user for each recommendation model according to each model weight; distributing corresponding content request quantity for each recommendation model according to the content acquisition proportion so as to determine the target recommendation content according to the content request quantity.
In an exemplary embodiment of the present disclosure, the first content recommendation unit includes a first weight determination subunit configured to perform: obtaining historical recommendation data of each recommendation model in the first combination model; the historical recommendation data includes a plurality of recommendation influencing factors; determining the factor weight of each recommendation influence factor according to the historical recommendation data; performing a weighted calculation based on a plurality of the factor weights to determine the model weights.
In one exemplary embodiment of the present disclosure, the first content recommendation unit includes a second weight determination subunit configured to perform: determining a content tag of each recommendation model in the first merging model; determining the model priority of each recommendation model according to the content tag; and determining the model weight corresponding to each recommended model according to the model priority.
In an exemplary embodiment of the present disclosure, the content recommendation apparatus further includes a third content recommendation module configured to perform: determining an associated user associated with the first user, generating an associated user group based on the first user and the associated user; responding to the model training operation of any user in the associated user group, and determining associated group recommended content corresponding to the associated user group; and displaying the associated group recommendation content to any user in the associated user group.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the content recommendation method of any one of the above.
According to a fifth aspect of the present disclosure, there is provided a computer-readable storage medium, wherein instructions, when executed by a processor of an electronic device, enable the electronic device to perform any one of the above-mentioned content recommendation methods.
According to a sixth aspect of embodiments of the present disclosure, there is provided a computer program product comprising computer programs/instructions, wherein the computer programs/instructions, when executed by a processor, implement the content recommendation method of any one of the above.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
according to the content recommendation method, on one hand, a user can independently select the recommendation model to conduct model training, and the accuracy rate of recommended content distribution is improved in a mode of independently and quickly learning the recommendation model. On the other hand, the user does not belong to an imperceptible state to the recommendation model any more, the autonomously controllable recommendation algorithm model learning capacity is provided for the user, and the control feeling of the user can be greatly improved. On the other hand, the adaptive cost of the cold start user is greatly reduced by a mode that the user independently and quickly learns the recommendation algorithm, and the loss rate of the related user can be greatly reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
Fig. 1 is a flow chart illustrating a method of content recommendation according to an example embodiment.
FIG. 2 is an interface diagram illustrating a user opening a model sharing option, according to an exemplary embodiment.
FIG. 3 is a diagram illustrating a first user selecting a recommended model for model training based on a second user, according to an example embodiment.
FIG. 4 is a diagram illustrating a first user selecting a recommendation model for model training via a user interaction interface, according to an example embodiment.
FIG. 5 is an interface diagram illustrating a first user composing an associated user group with other users in accordance with an exemplary embodiment.
Fig. 6 is a block diagram illustrating a content recommendation system according to an example embodiment.
Fig. 7 is a block diagram illustrating a content recommendation device according to an example embodiment.
FIG. 8 schematically shows a block diagram of an electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in 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 above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Existing algorithmic recommendation systems may include collaborative filtering systems, content-based recommendation systems, and hybrid recommendation model systems, among others. Two major mainstream types of recommendation systems may include: a content-based recommendation system and a collaborative filtering system. The collaborative filtering system may build a model based on the user's historical behavior, such as what the user browsed, approved, or commented on, in combination with similar decisions of other users, and such a model may be used to predict what content the user may be interested in or how much the user is interested in. Content-based recommendation systems may utilize some discrete characteristics about the content to recommend similar content with similar properties.
However, the currently adopted recommendation algorithm greatly depends on the historical data of the user as a judgment basis, it is difficult for the "cold start" user and the "preference-changed" user to judge what the real needs are, and the recommendation algorithm can only continuously collect the use habits of the user to continuously train an algorithm model, which increases the use cost of the user and also increases the loss possibility of the user. For users, algorithm training is not sensible to the users, but has a very large influence on the content seen by the users, many users do not know how the recommended algorithm model is formed, cannot intervene the model, and the control feeling of the users on the model is weak.
Two user groups are taken as examples for explanation below: for a certain platform of initial users, the content seen by the users is recommended according to the cold-start content model, and for such users, the content may not be the content that the users want to see, but the users cannot intervene in the control. For a user of the conversion platform, the content of cold start is the content which is popular in the past many times, but for the user of the conversion platform, many contents are the content which has already been watched, and under the condition that the homogenization of the current content is serious, the recommendation model is very easy to cause the loss of the user.
Based on this, according to an embodiment of the present disclosure, a content recommendation method, a content recommendation apparatus, a content recommendation system, an electronic device, a computer-readable storage medium, and a computer program product are provided.
Fig. 1 is a flowchart illustrating a content recommendation method according to an exemplary embodiment, and as shown in fig. 1, the content recommendation method may be used in a computer device, wherein the computer device described in the present disclosure may include a mobile terminal device such as a mobile phone, a tablet computer, a notebook computer, a palm top computer, a Personal Digital Assistant (PDA), and a fixed terminal device such as a desktop computer. The exemplary embodiment is illustrated by applying the method to a computer device, and it is understood that the method may also be applied to a server, and may also be applied to a system including a computer device and a server, and is implemented by interaction of the computer device and the server. The method specifically comprises the following steps.
In step S110, a model selection operation of the first user is received, and at least one first recommended model is selected for presentation.
In some exemplary embodiments of the present disclosure, the first user may be a user who performs the recommendation model training operation autonomously, for example, the first user may be a new user who has just started using a certain recommendation platform. The model selection operation may be a user operation of the first user selecting the recommended model. The first recommendation model may be a recommendation model selectable by the first user.
For a certain recommendation platform, if the first user is not interested in the current recommended content and wants to change the content recommended to the first user by the recommendation platform, the first user can autonomously train the recommendation model, and at this time, the first user can select one or more first recommendation models to be displayed in the user interaction interface. For example, the first user may be a new user who has recently completed a platform registration operation for the recommendation platform; the first user may also be a user who wants to update the relevant recommended content in the recommendation platform. Specifically, the first user may obtain, as the first recommendation model, the recommendation model of the other user through a user homepage of the other user. The first user can also select a specific user from the personal user main page, and the recommendation model of the specific user is used as the first recommendation model. When a model selection operation of a first user is received, the user interaction interface can show the selected first recommendation model.
In step S120, in response to the model merging instruction of the first user, at least one first recommended model corresponding to the model merging instruction is merged to the current recommended model to generate a first merged model.
In some exemplary embodiments of the present disclosure, the model merging instruction may be a processing instruction corresponding to a model merging operation performed on a plurality of recommended algorithm models. The current recommendation model may be a recommendation algorithm model to which the first user initially corresponds. The first merging model may be a recommendation model obtained by performing model merging training on the first recommendation model and the current recommendation model.
After the first user performs the model selection operation, one or more first recommendation models are displayed in the user interaction interface, at this time, the first user can select other recommendation models which are to be merged and trained with the current recommendation model, and when the model merging operation of the first user is received, a corresponding model merging instruction is generated according to the model merging operation. The recommendation platform responds to a model merging instruction of a first user, merges one or more first recommendation models corresponding to the model merging instruction into a current recommendation model, and enables the first recommendation model and the current recommendation model to conduct model merging training so as to generate a first merging model.
For example, the current recommendation model corresponding to the first user is "recommendation algorithm 1", and the first user selects the recommendation algorithm models of other users, for example, "recommendation algorithm 2" is used as the first recommendation model, and then the "recommendation algorithm 2" and the "recommendation algorithm 1" may be subjected to combination training to obtain a model after the combination training, that is, the first combination model "recommendation algorithm 1+ recommendation algorithm 2".
In step S130, in response to a merge completion instruction of the first user, target recommended content is determined for the first user based on the obtained first merge model.
In some exemplary embodiments of the present disclosure, the merging completion instruction may be an instruction corresponding to a plurality of recommended algorithm model completion models after merging training. The target recommended content may be recommended content pushed by the recommendation platform to the first user according to the merging model, for example, the target recommended content may be recommended content determined according to the first merging model, and the target recommended content may include various types of information content such as videos, graphics and texts.
After the first user selects the first recommendation model, the first recommendation models may be merged into the current recommendation model one by one, and when the first user merges all the selected first recommendation models into the current recommendation model, a merge completion instruction is generated. And responding to a merging completion instruction of the first user, carrying out model merging training on the basis of the first recommendation model and the current recommendation model by the recommendation platform to obtain a first merging model, and determining target recommendation content for the user according to the obtained first merging model.
For example, when the recommended content corresponding to the "recommendation algorithm 1" is a sports-class content, and the recommended content corresponding to the "recommendation algorithm 2" is a music-class content, after the model merging processing, the obtained first merging model may push the recommended content of the "sports class + music-class" for the first user, that is, the target recommended content.
According to the content recommendation method in the embodiment, on one hand, a user can autonomously select a recommendation model to perform model training, and the accuracy of recommended content distribution is improved in a mode of autonomously and rapidly learning the recommendation model. On the other hand, the user does not belong to an imperceptible state to the recommendation model any more, the autonomously controllable recommendation algorithm model learning capacity is provided for the user, and the control feeling of the user can be greatly improved. On the other hand, the adaptive cost of the cold start user is greatly reduced by a mode that the user independently and quickly learns the recommendation algorithm, and the loss rate of the related user can be greatly reduced.
Next, the content recommendation method in the present exemplary embodiment will be further explained.
In an exemplary embodiment of the present disclosure, in response to a model removal instruction of the first user, removing the recommended models merged in the first merged model to generate a second merged model; and determining target recommended content for the first user based on the obtained second merging model in response to the removal completion instruction of the first user.
Wherein the model removal instruction may be an operation instruction to remove the specified recommendation model from the first merged model. The second merging model may be a recommendation model obtained by removing one or more merged recommendation models in the first merging model through the model removal instruction. Only one recommendation algorithm model may be included in the second merging model. The target recommended content may also be recommended content pushed by the recommendation platform to the first user based on the second merging model.
When the recommendation platform recommends content to the first user by adopting the first merging model, if the first user wants that the recommendation platform does not continue to push certain categories of recommended content to the first user, one or more merged recommendation models in the first merging model can be removed from the first merging model through a model removing operation. When the first user performs the model removing operation, a corresponding model removing instruction is generated, and the recommendation platform can remove the recommendation model merged in the first merging model from the first merging model in response to the model removing instruction to obtain a second merging model. When the first user can remove the plurality of merged recommendation models from the first merged model through a plurality of model removing operations, a corresponding removal completion instruction can be generated when the first user completes the model removing operations. In response to the removal completion instruction of the first user, target recommended content may be determined for the first user based on the generated second merging model.
For example, after multiple model merging trainings performed by the first user, the current first merging model pushes recommended contents of music, movies, sports, favorite, and the like to the first user. At this time, when the first user no longer wants to receive recommended content of the movie and the favorite, the recommendation algorithm models respectively corresponding to the movie and the favorite are removed from the first merging model through the model removing instruction, the second merging model currently corresponding to the first user only includes the recommendation algorithm models of the music and the sports, and at this time, the target recommended content can be determined for the first user according to the recommendation algorithm models of the music and the sports. The matching degree of the recommended content can be further improved by the correlation processing operation in response to the model removal instruction.
In an exemplary embodiment of the present disclosure, the first recommendation model is a recommendation model corresponding to the second user; the second user is the user who opens the model sharing option.
The second user may be a user having a certain association with the first user, for example, the second user may be a user in a friend list of the first user, the second user may also be a user concerned by the first user, and the second user may also be a user with a top-ranked attention amount in the recommendation platform (e.g., a cyber red user). The recommendation model corresponding to the second user may be a recommendation algorithm model used by the recommendation platform to push recommended content for the second user. The model sharing option can be a configuration option for sharing the recommended algorithm model used by the user to other users for model combination training.
In order to solve the problem that model optimization cycle is long because historical data of a user needs to be continuously analyzed to perform model training in the process of model training and learning in the existing content recommendation scheme, the active training content recommendation scheme is provided, and specifically, a recommendation platform can provide a configuration item for the user to independently select whether to open a content recommendation model used by the user, namely a model sharing option. If a certain user selects to open the model sharing option, other users can independently choose to learn the recommendation model of the user so as to train the algorithm recommendation model of the user. Referring to FIG. 2, FIG. 2 is an interface diagram illustrating a user opening a model sharing option, according to an exemplary embodiment. For example, a first user in the recommendation platform may select an on model sharing option in "privacy settings" 220 of the "personal hub" section, such as switch 230 to turn off a "do not open my recommendation algorithm to others" configuration item, by clicking on the operation control 210 in the "personal hub" page of the first user. The "do not open my recommendation algorithm to others" configuration item switch is usually turned on by default in the recommendation platform, and if the first user turns off the option, the configuration item switch will be converted into the display style of the control 240. After a certain user opens the model sharing option, other users in the recommendation platform can acquire the recommendation model of the user to carry out model training.
When the first user performs the model selection operation through the second user, the first user may determine the second user from the friend list or the attention list of the first user, and the first user may select some users with higher attention as the second user through the recommendation platform. If the first user wants to acquire the recommendation model of the second user, the second user must be the user who has started the model sharing option in the recommendation platform, otherwise, the first user cannot acquire the recommendation model of the user. At this time, the first user may perform a model selection operation through the second user, and when the first user selects the recommendation model of the second user, the obtained recommendation model of the second user may be used as the first recommendation model, and the first recommendation model is displayed.
Referring to FIG. 3, FIG. 3 is a diagram illustrating a first user selecting a recommended model for model training based on a second user, according to an example embodiment. The first user may enter the own buddy list through the "personal center", select a certain user from the buddy list as the second user, and obtain the recommendation model of the second user, for example, by clicking the "obtain recommendation algorithm" control 320, to obtain the recommendation model of the second user for display. After the first user completes the model merging operation, in response to the user merging completion instruction, the recommendation platform performs a process of model merging training, and at this time, the user interaction interface displays a model training prompt message 330 of "please wait a little in the recommendation algorithm model learning". When the first recommendation model selected by the first user and the current recommendation model are combined and trained, the user interface displays an update completion prompt message 340, such as "may you, the model update is completed", and the like.
In an exemplary embodiment of the disclosure, a model selection operation performed by a first user based on a model selection area is received, and a model keyword corresponding to the model selection operation is determined; and selecting at least one first recommended model from the model library according to the model keywords and displaying the model on the interactive interface.
The model selection area can be an operation area provided by the recommendation platform for the first user to select the model. The model keywords may be keywords employed to determine a certain recommended model. The model library may be a database for storing recommended models.
The interactive interface of the recommendation platform may also provide the first user with a model selection area through which the first user may perform model selection operations. Referring to FIG. 4, FIG. 4 is a diagram illustrating a first user selecting a recommended model for model training via a user interaction interface, according to an example embodiment. As can be seen in FIG. 4, the first user may trigger the display of a model selection area 420 via an "add model" control 410 in the user interface. The first user may select one or more recommendation models directly from the model selection area 420, for example, the model selection area 420 may display recommendation models such as "sports," "budding pet," "cartoon," "movie," etc. for the first user to select.
In addition, the model selection area also provides an input area, and the first user can perform model selection operation by inputting characters. After the model selection operation of the first user is finished, the recommendation platform may determine the model keywords in the model selection operation performed by the first user, for example, the model keywords may include "lovely pet", "table tennis", "cate", "rap singer", and the like. The model selection area 430 displays the model keywords selected after the model selection operation of the first user is finished, and after the model keywords are determined, the recommendation platform selects the corresponding recommendation model from the model library and displays the recommendation model on the interactive interface, so that the first user selects one or more recommendation models to perform model merging operation. After the first user completes the model merging operation, in response to the merging completion instruction of the first user, the recommendation platform performs the model merging training process, and at this time, the model training prompt message 440 of "please wait a little in recommending algorithm model learning" may also be displayed in the user interface. By providing the model selection area, the autonomy of the first user for selecting the recommendation model and the control sense aiming at the recommendation model can be further improved, the adaptive cost of the user is reduced, and the loss rate of the user is reduced.
In an exemplary embodiment of the present disclosure, a model weight of each recommended model in the first merged model is determined; determining the content acquisition proportion of the first user for each recommended model according to the model weight; and distributing corresponding content request quantity for each recommendation model according to the content acquisition proportion so as to determine the target recommendation content according to the content request quantity.
Wherein the model weight may be the importance of each recommended model in the first merged model. The content acquisition weight may be a weight by which the recommendation platform acquires the recommended content according to the recommendation model. The content request quantity may be a request quantity corresponding to the recommendation platform pushing the recommended content to the first user.
After the first merging model is determined, model weights corresponding to the recommendation models in the first merging model may be further determined, so as to determine, according to the determined model weights, a proportion of the first user to obtain from the content of each recommendation model in the first merging model. For example, when the first merging model includes four recommendation models, model weights corresponding to the four recommendation models are determined respectively, and since the recommendation platform may include a large number of recommendation models, the model weights of the recommendation models may be determined according to historical recommendation effect data, and therefore, a sum of the model weights of the four recommendation models may not be 1. At this time, a reduction calculation may be performed according to the model weights of the four recommendation models to determine a content acquisition weight corresponding to each recommendation model for the first user, for example, the determined content acquisition weight may be 0.5,0.3,0.1, and at this time, if the recommendation platform needs to push one hundred pieces of recommendation content to the first user, the content request numbers corresponding to the four recommendation models are 50,30,10, and 10, respectively. For another example, when the model weights corresponding to the four recommendation models are the same, the number of content requests corresponding to each recommendation model is 25. The accuracy of the determined recommended content can be further improved by determining the final content request number through the model weight.
In an exemplary embodiment of the present disclosure, historical recommendation data of each recommendation model in a first merging model is obtained; the historical recommendation data includes a plurality of recommendation influencing factors; determining the factor weight of each recommendation influence factor according to historical recommendation data; a weighted calculation is performed based on the plurality of factor weights to determine the model weights.
The historical recommendation data can be recommendation effect data generated when the recommendation model carries out content recommendation. The recommendation influencing factor may be a relevant factor influencing the recommendation effect. For example, the recommendation influencing factors may include the viewing duration, interaction frequency, viewing amount, number of comments, number of likes, etc. of the user for the video. The factor weight may be a weight corresponding to each recommended influencing factor.
When calculating the model weights of the recommendation models, historical recommendation data of each recommendation model in the first merging model may be obtained first, where the historical recommendation data may include related data of all recommendation influence factors, for example, corresponding recommendation contents may be determined according to each recommendation model, and a user may view, comment, and forward the recommendation contents, so that related data such as viewing duration, interaction frequency, viewing number, like number, and comment number may be generated. When determining the model weight, the factor weight corresponding to each recommendation influence factor may be determined first, for example, the viewing number and the viewing duration have a large influence on the recommendation effect, so that the viewing duration and the viewing number may be configured with a large weight value, and the comment number, the like, and the interaction frequency have a small influence on the recommendation effect, and then a relatively small weight value may be configured correspondingly. After the factor weight of each recommended influence factor is determined, weighting calculation can be performed according to all the recommended influence factors and the corresponding factor weights, and finally the corresponding model weight is determined.
In an exemplary embodiment of the present disclosure, a content tag of each recommendation model in the first merging model is determined; determining the model priority of each recommendation model according to the content label; and determining the model weight corresponding to each recommended model according to the model priority.
The content tag may be a tag of recommended content corresponding to the recommendation model, for example, the content tag may be a game, a body-building, a movie, a fun, a synthesis, and the like. The model priority may be a priority used when content recommendation is performed using the recommendation model.
When determining the model weight of each recommendation model in the first merging model, the content tag of each recommendation model may be obtained, the recommendation platform may set a default priority for each content tag, and then determine the model priority of each recommendation model according to the content tag. For example, the recommendation platform may set a higher priority to content tags such as "fitness," "movie," "laugh," and so on. After the model priorities of different recommendation models are determined, the model priorities can be ranked, and the model weights of the recommendation models are determined according to ranking results. In addition, in the process of recommending the content by the recommendation platform, the first user may change the user preference setting, for example, the first user may reset the preference as the content such as the entertainment class, the game class, and the like, and then the recommendation platform may change the model priority of the corresponding recommendation model and recommend the content according to the changed model priority. The merge model will continue to sort and filter the recommended content to improve the accuracy of the recommended content.
Further, when the user initially uses a certain platform, the initial model weights of the recommendation models are the same, and at this time, the final model weights of the recommendation models can be determined according to the sequence of the label priorities of the default content labels in the system. The default tag priority may be determined by the recommendation platform based on user preferences of most users, or may be determined based on the order of selection when the user selects the recommendation model. For example, there are three recommendation models in the first merge model, the label priorities of the three recommendation models are high, medium, and low, respectively, and the model weights of the three recommendation models may be configured to be 50%, 30%, and 20%, respectively.
In an exemplary embodiment of the present disclosure, an associated user associated with a first user is determined, an associated user group is generated based on the first user and the associated user; determining associated group recommended content corresponding to the associated user group in response to model training operation of any user in the associated user group; and displaying the associated group recommendation content to any user in the associated user group.
Wherein the associated user may be other users having a specific association with the first user. The associated user group may be a user set composed of a plurality of users having a specific association relationship. The model training operation may be any operation in which the user selects the recommended model for merged training. The associated group recommendation content may be recommendation content pushed to the associated group by the recommendation platform.
For the first user, the associated users having a specific association relationship with the first user can be determined from the recommendation platform, and the first user and the associated users are used as the same group of users to generate a corresponding associated user group. And if any user in the associated user group performs model training operation, for example, one or more recommendation models are selected to perform combined training operation, determining the recommendation content of the associated group pushed to the associated user group according to the model training operation, and displaying the recommendation content of the associated group to all users in the associated group. Referring to FIG. 5, FIG. 5 is an interface diagram illustrating a first user composing an associated user group with other users according to an example embodiment. The first user can enter the friend interface through the operation control 510 of the "personal center", and a friend list 520 corresponding to the first user is displayed in the friend interface. When the first user wants to use the friend 1 as an associated user to jointly generate an associated user group, the interaction button 521 corresponding to the friend 1 is clicked, at this time, the associated friend operation interface 530 is displayed, prompt information indicating whether the friend 1 is added as the associated user is displayed in the associated friend operation interface 530, and if the prompt information indicates that the friend 1 is added as the associated user, the first user can establish an associated relationship with the friend 1 by clicking the "confirmation" control 531 to generate a corresponding associated user group. At this time, all users in the associated user group share the recommendation model, at this time, the first user may be displayed with the prompt information 540 of "please wait for a little during the learning of the recommendation algorithm model", and after the model merging training is finished, the recommendation platform pushes the same target recommendation content to any user of the associated user group.
For example, in a content push scenario, if two accounts are bound as lovers 'accounts to obtain recommended content, the two accounts will be users in the same associated user group, when any one user of the associated user group performs model training operation, corresponding recommended content will be generated according to the corresponding model training operation, and the generated recommended content will be respectively pushed to any one user of the lovers' accounts, and the recommended content of the two accounts can be always consistent. By providing the function of associating the user groups, the user interaction in the recommendation platform can be improved, and the user viscosity is effectively improved.
In summary, the content recommendation method of the present disclosure receives a model selection operation of a first user, and selects at least one first recommendation model for display; responding to a model merging instruction of a first user, merging at least one first recommendation model corresponding to the model merging instruction to a current recommendation model to generate a first merging model; and in response to the merging completion instruction of the first user, determining target recommended content for the first user based on the obtained first merging model. On one hand, the user can independently select the recommendation model to carry out model training, and the accuracy of recommendation content distribution is improved in a mode of independently and quickly learning the recommendation model. On the other hand, the user does not belong to an imperceptible state to the recommendation model any more, the autonomously controllable recommendation algorithm model learning capacity is provided for the user, and the control feeling of the user can be greatly improved. On the other hand, the adaptive cost of the cold start user is greatly reduced by a mode that the user independently and quickly learns the recommendation algorithm, and the loss rate of the related user can be greatly reduced.
According to a second aspect of the embodiments of the present disclosure, there is provided a content recommendation system, and referring to fig. 6, fig. 6 is a block diagram illustrating a content recommendation system according to an exemplary embodiment. The content recommendation system 600 includes: a user terminal 610, a model training terminal 620, and a content recommendation platform 630. Specifically, the method comprises the following steps:
the user terminal 610 is used for providing a model operation interface, displaying target recommended content and receiving model operation performed by a user based on the model operation interface; the model operation comprises a model selection operation, a model removal operation and a model sharing operation; the model training terminal 620 is configured to merge the first recommendation model determined based on the model selection operation into the current recommendation model to generate a first merged model; removing at least one recommended model in the first merging model according to the model removing operation of the first merging model to generate a second merging model; and the content recommendation platform 630 is configured to determine target recommended content for the user according to the merging model, and send the target recommended content to the user side.
The model operation interface can be a user interaction interface used for performing relevant model operation. The model operation may be a user operation performed on the recommended model. The model selection operation may be a related operation of the user selecting one or more recommended models. The model removal operation may be a related operation in which the user removes one or more recommended models from the merged model. The model sharing operation may be a related operation in which a user shares a recommendation model adopted by the user to other users, so that the other users can perform model merging training based on the shared recommendation model.
The user side can provide a user model operation interface, the user carries out model sharing operation through the model operation interface, and a model sharing option is started, so that other users can obtain the recommended algorithm model of the user side. The user can also perform model selection operation through the model operation interface to obtain the recommendation models of other users so as to train the recommendation models of the user quickly and purposefully. When the user selects the first recommendation model through the model selection operation, the model training terminal may merge the first recommendation model into the current recommendation model to generate a first merged model. After generating the first merging model, the content recommendation platform determines target recommended content to the user according to the first merging model. In the process of receiving recommended content, if a user wants to reduce the push of some recommended content, the corresponding recommended model can be removed through model removal operation to obtain a second merging model, and at this time, the content recommendation platform determines target recommended content to the user according to the second merging model.
Fig. 7 is a block diagram illustrating a content recommendation device according to an example embodiment. Referring to fig. 7, the content recommendation apparatus 700 includes a model presentation module 710, a model merging module 720, and a first content recommendation module 730. Specifically, the method comprises the following steps:
and the model presentation module 710 is configured to perform a model selection operation of receiving the first user and select at least one first recommended model for presentation.
And the model merging module 720 is configured to execute a model merging instruction in response to the first user, merge at least one first recommended model corresponding to the model merging instruction to the current recommended model to generate a first merged model.
The first content recommendation module 730 is configured to execute determining a target recommended content for the first user based on the obtained first merging model in response to the merging completion instruction of the first user.
In an exemplary embodiment of the present disclosure, the content recommendation apparatus 700 further includes a second content recommendation module configured to perform: removing the recommended models combined in the first combination model in response to the model removing instruction of the first user to generate a second combination model; and determining target recommended content for the first user based on the obtained second merging model in response to the removal completion instruction of the first user.
In an exemplary embodiment of the present disclosure, the model presentation module 710 further includes a model presentation unit configured to perform: receiving model selection operation performed by a first user based on the model selection area, and determining model keywords corresponding to the model selection operation; and selecting at least one first recommended model from the model library according to the model keywords and displaying the model on the interactive interface.
In an exemplary embodiment of the present disclosure, the first content recommendation module 730 includes a first content recommendation unit configured to perform: determining model weights of recommended models in the first merging model; determining the content acquisition proportion of the first user for each recommended model according to the model weight; and distributing corresponding content request quantity for each recommendation model according to the content acquisition proportion so as to determine the target recommendation content according to the content request quantity.
In an exemplary embodiment of the present disclosure, the first content recommendation unit includes a first weight determination subunit configured to perform: acquiring historical recommendation data of each recommendation model in the first merging model; the historical recommendation data includes a plurality of recommendation influencing factors; determining the factor weight of each recommendation influence factor according to historical recommendation data; a weighted calculation is performed based on the plurality of factor weights to determine the model weights.
In one exemplary embodiment of the present disclosure, the first content recommendation unit includes a second weight determination subunit configured to perform: determining content labels of all recommendation models in the first merging model; determining the model priority of each recommendation model according to the content label; and determining the model weight corresponding to each recommended model according to the model priority.
In an exemplary embodiment of the present disclosure, the content recommendation apparatus 700 further includes a third content recommendation module configured to perform: determining an associated user associated with the first user, and generating an associated user group based on the first user and the associated user; determining associated group recommended content corresponding to the associated user group in response to model training operation of any user in the associated user group; and displaying the associated group recommendation content to any user in the associated user group.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
An electronic device 800 according to such an embodiment of the disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.
As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one memory unit 820, a bus 830 connecting different system components (including the memory unit 820 and the processing unit 810), and a display unit 840.
Wherein the storage unit stores program code that is executable by the processing unit 810 to cause the processing unit 810 to perform steps according to various exemplary embodiments of the present disclosure as described in the "exemplary methods" section above in this specification.
The storage unit 820 may include readable media in the form of volatile storage units, such as a random access storage unit (RAM)821 and/or a cache storage unit 822, and may further include a read only storage unit (ROM) 823.
The electronic device 800 may also communicate with one or more external devices 870 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. As shown, the network adapter 860 communicates with the other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
In an exemplary embodiment, a computer-readable storage medium comprising instructions, such as a memory comprising instructions, executable by a processor of an apparatus to perform the above-described method is also provided. Alternatively, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, there is also provided a computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the content recommendation method of any of the above.
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 application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the 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 will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A content recommendation method, comprising:
receiving model selection operation of a first user, and selecting at least one first recommendation model for displaying;
responding to a model merging instruction of the first user, merging at least one first recommendation model corresponding to the model merging instruction to a current recommendation model to generate a first merging model;
and responding to a merging completion instruction of the first user, and determining target recommended content for the first user based on the obtained first merging model.
2. The method of claim 1, further comprising:
removing the merged recommendation model in the first merging model to generate a second merging model in response to the model removing instruction of the first user;
and in response to a removal completion instruction of the first user, determining target recommended content for the first user based on the obtained second merging model.
3. The method of claim 1, further comprising:
the first recommendation model is a recommendation model corresponding to a second user; the second user is a user who opens a model sharing option.
4. The method of claim 1, wherein determining the target recommended content for the first user based on the obtained first merged model comprises:
determining a model weight of each recommended model in the first merging model;
determining the content acquisition proportion of the first user for each recommendation model according to each model weight;
distributing corresponding content request quantity for each recommendation model according to the content acquisition proportion so as to determine the target recommendation content according to the content request quantity.
5. The method according to any one of claims 1-4, further comprising:
determining an associated user associated with the first user, generating an associated user group based on the first user and the associated user;
responding to the model training operation of any user in the associated user group, and determining associated group recommended content corresponding to the associated user group;
and displaying the associated group recommendation content to any user in the associated user group.
6. A content recommendation system, comprising:
the user side is used for providing a model operation interface, displaying target recommended content and receiving model operation performed by a user based on the model operation interface; the model operation comprises a model selection operation, a model removal operation and a model sharing operation;
the model training end is used for merging the first recommendation model determined based on the model selection operation into the current recommendation model to generate a first merging model; removing at least one recommended model in the first merging model according to the model removing operation of the first merging model to generate a second merging model;
and the content recommendation platform is used for determining target recommendation content for the user according to the merging model and sending the target recommendation content to the user side.
7. A content recommendation apparatus characterized by comprising:
the model display module is configured to execute model selection operation of a first user and select at least one first recommended model for display;
the model merging module is configured to execute a model merging instruction responding to the first user, and merge at least one first recommended model corresponding to the model merging instruction into a current recommended model to generate a first merged model;
a first content recommendation module configured to execute a merge completion instruction in response to the first user, and determine a target recommended content for the first user based on the obtained first merge model.
8. 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 content recommendation method of any one of claims 1 to 5.
9. A computer-readable storage medium in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform the content recommendation method of any one of claims 1-5.
10. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the content recommendation method of any one of claims 1 to 5.
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