CN110555135A - Content recommendation method, content recommendation device and electronic equipment - Google Patents
Content recommendation method, content recommendation device and electronic equipment Download PDFInfo
- Publication number
- CN110555135A CN110555135A CN201810258813.9A CN201810258813A CN110555135A CN 110555135 A CN110555135 A CN 110555135A CN 201810258813 A CN201810258813 A CN 201810258813A CN 110555135 A CN110555135 A CN 110555135A
- Authority
- CN
- China
- Prior art keywords
- content
- label
- tag
- topic
- similarity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to a content recommendation method, a content recommendation device and an electronic device. The content recommendation method comprises the following steps: acquiring behavior data of a user on first content and second content with the same type and different subtypes; determining at least one first tag of the first content and at least one second tag of the second content; obtaining the topic similarity of each label in at least one first label and at least one second label and an implied topic by an implied Dirichlet allocation model; obtaining the label similarity between the first label and the second label based on the topic similarity; and providing the content to the user based on the tag similarity. Therefore, the portrait migration of the user can be performed by utilizing the heterogeneous data of the user, the accuracy of user interest prediction is improved, and the user experience is optimized.
Description
Technical Field
The present application relates generally to the field of data processing, and more particularly, to a content recommendation method, a content recommendation apparatus, and an electronic device.
background
With the development of the cultural industry, more and more contents are produced to meet the increasing demands of people. Also, as the content presentation form is enriched, more and more contents are presented in a form such as multimedia.
when a user needs to obtain content required by the user, the user needs to spend a lot of time on finding the content required by the user aiming at massive content on the network. This process obviously causes inconvenience to the user if the user needs to browse through a large amount of irrelevant content.
in particular, even for the same type of content, such as video, more elaborate sub-types appear as their types are further subdivided. For example, many platforms classify videos into UGC (user produced content), PGC (professional produced content), and OGC (brand produced content).
therefore, it is necessary to recommend content that may be of interest to the user according to the user's interest characteristics. In particular, when only the interest characteristics of the user for a certain sub-type of content are grasped, it is also necessary to recommend other sub-types of content to the user accordingly.
accordingly, there is a need for improved content recommendation schemes.
Disclosure of Invention
The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides a content recommendation method, a content recommendation device and electronic equipment, which can perform portrait migration on a user by using heterogeneous data of the user, improve the accuracy of user interest prediction and optimize user experience.
according to an aspect of the present application, there is provided a content recommendation method including: acquiring behavior data of a user on first content and second content, wherein the first content and the second content have the same type and different subtypes; determining at least one first tag of the first content and at least one second tag of the second content; obtaining a topic similarity of each of the at least one first label and the at least one second label to an implied topic with an implied dirichlet allocation model; obtaining a tag similarity between the first tag and the second tag based on a topic similarity of each of the at least one first tag and the at least one second tag to an implied topic; and providing content to the user based on the tag similarity.
In the content recommendation method, the acquiring behavior data of the user for the first content and the second content includes: behavior data of a user for the first content and the second content within a predetermined period of time is acquired.
In the content recommendation method, the training process of the implicit dirichlet allocation model is as follows: generating a content sequence of the first and second content and a tag sequence of the at least one first tag and at least one second tag for the predetermined period of time; inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model; setting one or more implicit themes for training; and obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
in the content recommendation method, obtaining the tag similarity between the first tag and the second tag based on the topic similarity between each of the at least one first tag and the at least one second tag and an implied topic includes: multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and summing products of the first label and the second label for the same implied subject to obtain a label similarity between the first label and the second label.
In the above content recommendation method, the first content and the second content are video contents; the first content is one of user production content, professional production content, and brand production content; and the second content is another one of user production content, professional production content, and brand production content.
According to another aspect of the present application, there is provided a content recommendation apparatus including: a data acquisition unit configured to acquire behavior data of a user for first content and second content, the first content and the second content having a same genre and different sub-genres; a tag determination unit for determining at least one first tag of the first content and at least one second tag of the second content; a model calculation unit, configured to obtain a topic similarity of each of the at least one first tag and the at least one second tag to an implied topic with an implied dirichlet allocation model; a similarity calculation unit, configured to obtain a tag similarity between each of the at least one first tag and the at least one second tag and an implied subject based on a subject similarity of the first tag and the second tag; and the content providing unit is used for providing the content to the user based on the label similarity.
In the above content recommendation device, the data acquisition unit is configured to acquire behavior data of the user for the first content and the second content within a predetermined period.
In the content recommendation apparatus, the training process of the implicit dirichlet allocation model is as follows: generating a content sequence of the first and second content and a tag sequence of the at least one first tag and at least one second tag for the predetermined period of time; inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model; setting one or more implicit themes for training; and obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
In the above content recommendation device, the similarity calculation unit is configured to: multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and summing products of the first label and the second label for the same implied subject to obtain a label similarity between the first label and the second label.
In the above content recommendation apparatus, the first content and the second content are video contents; the first content is one of user production content, professional production content, and brand production content; and the second content is another one of user production content, professional production content, and brand production content.
according to yet another aspect of the present application, there is provided an electronic device including: a processor; and a memory in which computer program instructions are stored, which, when executed by the processor, cause the processor to perform the content recommendation method as described above.
The content recommendation method, the content recommendation device and the electronic equipment can utilize heterogeneous data of the user to perform portrait migration of the user, accuracy of user interest prediction is improved, and user experience is optimized.
Drawings
Various other advantages and benefits of the present application will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. It is obvious that the drawings described below are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. Also, like parts are designated by like reference numerals throughout the drawings.
FIG. 1 illustrates a flow chart of a method of content recommendation according to an embodiment of the present application;
FIG. 2 illustrates a flow diagram of LDA model training in a content recommendation method according to an embodiment of the present application;
FIG. 3 illustrates a block diagram of a content recommendation device according to an embodiment of the present application;
FIG. 4 illustrates a block diagram of a model training unit in a content recommendation device according to an embodiment of the present application;
FIG. 5 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.
summary of the application
As described above, with the continuous enrichment of content forms, even for a certain type of content, such as video, different video subtypes (e.g., UGC video, PGC video, or OGC video) appear on video content for respective video platforms. And, on the video platform, the UGC video can be recommended to the user, and the PGC video can also be recommended to the user, so that different requirements of the user are met. However, in the recommendation strategy, videos of different sub-types, such as UGC videos and PGC videos, have a certain difference in video content, so that the difference in content labels and content distribution for the recommended videos is large. Such content data of different sub-types may also be referred to as heterogeneous data.
therefore, it is very important how to recommend the content of another sub-type that the user is interested in by using the behavior of the user for the content of a certain sub-type in the content platform, so as to meet the requirements of the user for the content of different sub-types.
In view of the above technical problems, the basic idea of the present application is to collect behavior data of a user for different subtypes of content, for example, viewing behavior data of videos viewing different subtypes, and perform implicit dirichlet allocation (LDA) model training based on the viewing behavior data, so as to obtain tag similarity between tag data of different subtypes of content, so as to recommend the content to the user. Therefore, after the tag similarity between the tag data of the contents of different sub-types is obtained, the tag portrait of the user for the contents of another sub-type can be calculated for the tag portrait of the user of the contents of a certain sub-type, so that portrait migration of the user is realized, and the contents in which the user is interested are recommended.
Based on the above, the application provides a content recommendation method, a content recommendation device and an electronic device, which first obtain behavior data of a user for different sub-types of content, then determine a label corresponding to the content, and obtain similarity between different labels through an LDA model by taking the content and the label as input, so as to recommend the content to the user. Therefore, portrait migration of the user can be performed by utilizing the behavior data of the user aiming at the contents of different sub-types, so that the accuracy of predicting the preference of the user for the contents of different sub-types is improved, and the user experience is improved.
It should be noted that the above basic concept of the present application can be applied to various recommendation systems and corresponding products for recommending contents, including recommendation systems and corresponding products for video content, audio content, text content, and other contents.
having described the general principles of the present application, various non-limiting embodiments of the present application will now be described with reference to the accompanying drawings.
Exemplary method
Fig. 1 illustrates a flow chart of a content recommendation method according to an embodiment of the present application.
As shown in fig. 1, a content recommendation method according to an embodiment of the present application includes: s110, acquiring behavior data of a user on first content and second content, wherein the first content and the second content have the same type and different sub-types; s120, determining at least one first label of the first content and at least one second label of the second content; s130, obtaining the topic similarity of each label in the at least one first label and the at least one second label and an implied topic by using an implied Dirichlet allocation model; s140, obtaining the label similarity between the first label and the second label based on the topic similarity between each label of the at least one first label and the at least one second label and the implicit topic; and S150, providing the content to the user based on the label similarity.
in step S110, behavior data of a user for first content and second content, the first content and the second content having the same genre and different sub-genres, is acquired. As mentioned above, due to the further subdivision of the content, even for the same type of content, e.g. video, it may be subdivided into different sub-types. For example, videos provided by respective video platforms to users may be subdivided into UGC (user produced content), PGC (professional produced content), and OGC (brand produced content).
Specifically, UGC refers to user-originated content, which is emerging along with the concept of web2.0 (web2.0) advocating personalization as a key feature. The UGC reflects not only a specific service, but also a new way for a user to use the Internet, namely, downloading is changed into downloading and uploading again from original downloading. PGCs refer to professionally produced content (e.g., video websites) or expert produced content (e.g., microblogs). The method is characterized by content personalization, view angle diversification, democratization propagation and social relationship virtualization. OGC refers to brand production content, which is produced primarily by industry people with certain knowledge and professional backgrounds, and these people receive corresponding rewards. For example, journalists, editors, etc. of the media platform have both professional backgrounds for news and consideration for professions in writing.
Therefore, in the content recommendation method according to the embodiment of the present application, the first content and the second content are content of the same type, for example, both belong to video, audio, text, or the like, but further subdivided subtypes thereof are different, for example, in the case where the first content and the second content are both videos, the first content may be one of UGC, PGC, and OGC videos as described above, and the second content is the other one of UGC, PGC, and OGC videos.
In addition, current content platforms provide content to users, as well as push advertisements to users, e.g., video platforms push video advertisements to users. Moreover, in order to improve the accuracy of advertisement pushing, the advertisement is also provided with a label. Therefore, in the content recommendation method according to the embodiment of the present application, the first content and the second content may also be general content and advertisement.
In the content recommendation method according to the embodiment of the application, the behavior data of the user for the first content and the second content refers to a behavior of the user for selecting a specific content based on the recommendation of the recommendation system, and taking a video as an example, the behavior of the user for watching the specific video. Taking the viewing behavior of the user on the UGC and PGC videos as an example, uv may be used to represent the UGC video and pv may be used to represent the PGC video, and the behavior data of the user may be expressed as h ═ { pv1, pv2, …, pvN, uv1, uv2, …, uvM }.
Here, in order to accurately reflect the behavior of the user, the behavior data of the user with respect to the first content and the second content refers to the behavior data of all users using the content platform. That is, the content platform may obtain the behavior data of all users over a period of time, e.g., the video platform obtains the behavior data of all users watching a particular video in the last 15 days.
However, since the behavior data of the user for the content reflects the user's interest in the content, the user's interest in the content is not constant. For example, if a user watches a video on a video website, the animation-related video is watched a month ago, but the game-related video may start to be watched a month later. In order to obtain relatively stable interests of a user, in the content recommendation method according to the embodiment of the application, the short-time stable interests of the user are obtained through time segmentation. That is, it is assumed that the user's interest is stable for a short time but is changed over a long time. Thus, the behavior of the content over a period of time may be segmented into a plurality of behavior sequences based on the behavior of the user for the content.
That is, in the content recommendation method according to the embodiment of the present application, acquiring behavior data of the user for the first content and the second content includes: behavior data of a user for the first content and the second content within a predetermined period of time is acquired.
here, the predetermined period may be 12 hours, that is, the behavior of the user with respect to the content is divided into a plurality of behavior sequences, for example, video viewing sequences, and denoted as hs, at intervals of 12 hours.
In step S120, at least one first tag of the first content and at least one second tag of the second content are determined. For example, for the above video viewing sequence hs ═ { pv1, pv2, …, pvN, uv1, uv2, …, uvM }, a corresponding tag sequence is determined, for example, denoted as hst ═ pt1, pt2, …, ptN, ut1, ut2, …, utM }, where pt denotes PGC video tags and ut denotes UGC video tags.
In step S130, a topic similarity of each of the at least one first label and the at least one second label to an implied topic is obtained in an implied dirichlet allocation (LDA) model. Here, the LDA model is a document theme generation model, also called a three-layer bayesian probability model, and includes three layers of structures of words, themes and documents. For the generative model, each word of an article is considered to be obtained through a process of "selecting a topic with a certain probability and selecting a word from the topic with a certain probability". Also, document-to-topic follows a polynomial distribution, and topic-to-word follows a polynomial distribution.
In the content recommendation method according to the embodiment of the application, an LDA model is trained through behavior data of a user to obtain a mapping relation between a label and a theme. Since the LDA model is used to train the relevance of articles and words, that is, an article is a training sample, and the corresponding word in the article is used as the basic unit of the model. In the content recommendation method according to the embodiment of the application, a behavior sequence of a user for content is used as an article, and a label corresponding to the content is used as a basic unit of a model. For example, for the PGC video content and the UGC video content as described above, the video sequence generated is hs ═ { pv1, pv2, …, pvN, uv1, uv2, · uvM }, and the corresponding tag sequence is hst ═ { pt1, pt2, …, ptN, ut1, ut2, …, utM }, which is input as a model.
Next, one or more implicit themes for training, i.e., themes of the LDA model, are set and trained. In this way, the topic similarity of each tag to each implied topic, denoted as p (tag | topic), can be obtained.
that is to say, in the content recommendation method according to the embodiment of the present application, the training process of the implicit dirichlet allocation model is as follows: generating a content sequence of the first and second content and a tag sequence of the at least one first and at least one second tag for a plurality of predetermined periods of time; inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model; setting one or more implicit themes for training; and obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
Fig. 2 illustrates a flowchart of LDA model training in a content recommendation method according to an embodiment of the present application. As shown in fig. 2, the training process of the LDA model includes: s210, generating content sequences of the first content and the second content and label sequences of the at least one first label and the at least one second label in a plurality of preset time periods; s220, inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, wherein the first content and the second content are used as training samples, and the at least one first label and the at least one second label are used as basic units of a model; s230, setting one or more implicit themes for training; and S240, obtaining the theme similarity between each label of the at least one first label and the at least one second label and each implied theme.
Next, in step S140, a tag similarity between each of the at least one first tag and the at least one second tag and an implied topic is obtained based on the topic similarity of the first tag and the second tag.
specifically, after obtaining the subject similarity p (tag | topic) of each of the at least one first label and the at least one second label to each implied topic through the LDA model, for the first label pt and the second label ut, the label similarity between the first label pt and the second label ut may be calculated, for example, Sim (pt, ut) ═ sum (p (pt | topic)/(ut | topic)).
that is to say, in the content recommendation method according to the embodiment of the present application, obtaining the tag similarity between the first tag and the second tag based on the topic similarity between each of the at least one first tag and the at least one second tag and an implied topic includes: multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and summing products of the first label and the second label for the same implied subject to obtain a label similarity between the first label and the second label.
Finally, in step S150, content is provided to the user based on the tag similarity.
here, although the above description has been made by taking the example that the video platform recommends a video to a user. However, it will be understood by those skilled in the art that the content recommendation method according to the embodiment of the present application can be applied to various content platforms for recommending content to a user, and thus for performing migration of a user representation for specific content through other types of heterogeneous data. This application is not intended to be limiting in any way.
Exemplary devices
Fig. 3 illustrates a block diagram of a content recommendation device according to an embodiment of the present application.
As shown in fig. 3, a content recommendation device 300 according to an embodiment of the present application includes: a data acquisition unit 310 configured to acquire behavior data of a user for first content and second content, the first content and the second content having a same genre and different sub-genres; a tag determination unit 320 configured to determine at least one first tag of the first content and at least one second tag of the second content acquired by the data acquisition unit 310; a model calculating unit 330, configured to obtain a topic similarity between each of the at least one first tag and the at least one second tag determined by the tag determining unit 320 and an implied topic by using an implied dirichlet allocation model; a similarity calculation unit 340, configured to obtain a tag similarity between at least one first tag and at least one second tag obtained by the model calculation unit 330 based on a topic similarity between each of the first tag and the second tag and an implied topic; and a content providing unit 350 for providing the content to the user based on the tag similarity calculated by the similarity calculating unit 340.
In one example, in the content recommendation device 300 described above, the data acquisition unit 310 is configured to acquire behavior data of the user for the first content and the second content within a predetermined period.
In one example, in the content recommendation apparatus 300, the training process of the implicit dirichlet allocation model is as follows: generating a content sequence of the first and second content and a tag sequence of the at least one first and at least one second tag for a plurality of predetermined periods of time; inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model; setting one or more implicit themes for training; and obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
That is, in the content recommendation apparatus 300 according to an embodiment of the present application, a model training unit may be further included to train the LDA model.
Fig. 4 illustrates a block diagram of a model training unit in a content recommendation device according to an embodiment of the present application. As shown in fig. 4, the content recommendation apparatus 400 according to the embodiment of the present application includes a model training unit 410 for training an LDA model. The model training unit 410 includes: a data generating module 411, configured to generate a content sequence of the first content and the second content and a tag sequence of the at least one first tag and the at least one second tag for a plurality of predetermined periods; a data input module 412, configured to input the content sequence and the tag sequence as implicit dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first tag and the at least one second tag as basic units of a model; a topic setting module 413 for setting one or more implicit topics for training; and a calculating module 414 for obtaining a topic similarity of each of the at least one first label and the at least one second label to each implied topic.
In one example, in the content recommendation apparatus 300 described above, the similarity calculation unit 340 is configured to: multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and summing products of the first label and the second label for the same implied subject to obtain a label similarity between the first label and the second label.
In one example, in the above-described content recommendation apparatus 300, the first content and the second content are video content; the first content is one of user production content, professional production content, and brand production content; and the second content is another one of user production content, professional production content, and brand production content.
Here, it can be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described content recommendation apparatus 300 have been described in detail in the content recommendation method described above with reference to fig. 1 and 2, and thus, a repetitive description thereof will be omitted.
As described above, the content recommendation apparatus 300 according to the embodiment of the present application can be implemented in various terminal devices, for example, servers of various content platforms. In one example, the content recommendation device 300 according to the embodiment of the present application may be integrated into the terminal device as a software module and/or a hardware module. For example, the content recommendation apparatus 300 may be a software module in an operating system of the terminal device, or may be an application developed for the terminal device; of course, the content recommendation device 300 may also be one of many hardware modules of the terminal device.
Alternatively, in another example, the content recommendation apparatus 300 and the terminal device may be separate devices, and the content recommendation apparatus 300 may be connected to the authentication device through a wired and/or wireless network and transmit the interaction information according to an agreed data format.
Alternative embodiments
Here, as can be understood by those skilled in the art, the essence of the present application is to calculate the similarity between the labels of different contents and the topics through the LDA model, thereby obtaining the similarity between the labels of different contents. Therefore, the label similarity obtained in the above manner may be used for calculating other parameters or implementing other functions, such as indirectly determining the similarity of the content to implement content clustering, in addition to recommending the content.
therefore, in an alternative embodiment, an embodiment of the present application provides a tag similarity calculation method, including: acquiring behavior data of a user on first content and second content, wherein the first content and the second content have the same type and different subtypes; determining at least one first tag of the first content and at least one second tag of the second content; obtaining a topic similarity of each of the at least one first label and the at least one second label to an implied topic with an implied dirichlet allocation model; obtaining a tag similarity between the first tag and the second tag based on a topic similarity of each of the at least one first tag and the at least one second tag to an implied topic.
In the tag similarity calculation method, acquiring behavior data of the user for the first content and the second content includes: behavior data of a user for the first content and the second content within a predetermined period of time is acquired.
In the above label similarity calculation method, the training process of the implicit dirichlet allocation model is as follows: generating a content sequence of the first and second content and a tag sequence of the at least one first tag and at least one second tag for the predetermined period of time; inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model; setting one or more implicit themes for training; and obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
In the above tag similarity calculation method, obtaining the tag similarity between the first tag and the second tag based on the topic similarity between each of the at least one first tag and the at least one second tag and an implied topic includes: multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and summing products of the first label and the second label for the same implied subject to obtain a label similarity between the first label and the second label.
According to another aspect of the present application, there is provided a tag similarity calculation apparatus including: a data acquisition unit configured to acquire behavior data of a user for first content and second content, the first content and the second content having a same genre and different sub-genres; a tag determination unit for determining at least one first tag of the first content and at least one second tag of the second content; a model calculation unit, configured to obtain a topic similarity of each of the at least one first tag and the at least one second tag to an implied topic with an implied dirichlet allocation model; a similarity calculation unit, configured to obtain a tag similarity between each of the at least one first tag and the at least one second tag and an implied subject based on a topic similarity of the first tag and the second tag.
In the above tag similarity calculation apparatus, the data acquisition unit is configured to acquire behavior data of a user for the first content and the second content within a predetermined period.
In the above tag similarity calculation apparatus, the training process of the implicit dirichlet allocation model is as follows: generating a content sequence of the first and second content and a tag sequence of the at least one first tag and at least one second tag for the predetermined period of time; inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model; setting one or more implicit themes for training; and obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
In the above tag similarity calculation apparatus, the similarity calculation unit is configured to: multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and summing products of the first label and the second label for the same implied subject to obtain a label similarity between the first label and the second label.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 5.
FIG. 5 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
as shown in fig. 5, the electronic device 10 includes one or more processors 11 and memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 11 to implement the content recommendation methods of the various embodiments of the present application described above and/or other desired functions. Various contents such as content data, tag data, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 13 may be, for example, a keyboard, a mouse, or the like.
The output device 14 may output various information including contents recommended to the user to the outside. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 5, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the content recommendation method according to various embodiments of the present application described in the "exemplary methods" section above of this specification.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in a content recommendation method according to various embodiments of the present application described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, 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 include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
the foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
the previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (11)
1. a content recommendation method, comprising:
Acquiring behavior data of a user on first content and second content, wherein the first content and the second content have the same type and different subtypes;
Determining at least one first tag of the first content and at least one second tag of the second content;
obtaining a topic similarity of each of the at least one first label and the at least one second label to an implied topic with an implied dirichlet allocation model;
Obtaining a tag similarity between the first tag and the second tag based on a topic similarity of each of the at least one first tag and the at least one second tag to an implied topic; and
And providing content to the user based on the label similarity.
2. the content recommendation method of claim 1, wherein obtaining behavior data of the user for the first content and the second content comprises:
Behavior data of a user for the first content and the second content within a predetermined period of time is acquired.
3. The content recommendation method of claim 2, wherein the training process of the implicit Dirichlet allocation model is as follows:
generating a content sequence of the first and second content and a tag sequence of the at least one first and at least one second tag for a plurality of predetermined periods of time;
inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model;
setting one or more implicit themes for training; and
And obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
4. The content recommendation method of claim 3, wherein obtaining the tag similarity between the first tag and the second tag based on the topic similarity of each of the at least one first tag and the at least one second tag to an implied topic comprises:
Multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and
Summing products of the first label and the second label for the same underlying topic to obtain a label similarity between the first label and the second label.
5. The content recommendation method according to any one of claims 1 to 4,
The first content and the second content are video content;
The first content is one of user production content, professional production content, and brand production content; and
The second content is another one of user production content, professional production content, and brand production content.
6. A content recommendation apparatus comprising:
A data acquisition unit configured to acquire behavior data of a user for first content and second content, the first content and the second content having a same genre and different sub-genres;
A tag determination unit for determining at least one first tag of the first content and at least one second tag of the second content;
A model calculation unit, configured to obtain a topic similarity of each of the at least one first tag and the at least one second tag to an implied topic with an implied dirichlet allocation model;
A similarity calculation unit, configured to obtain a tag similarity between each of the at least one first tag and the at least one second tag and an implied subject based on a subject similarity of the first tag and the second tag; and
And the content providing unit is used for providing the content to the user based on the label similarity.
7. The content recommendation device according to claim 6, wherein the data acquisition unit is configured to acquire behavior data of a user for the first content and the second content within a predetermined period.
8. The content recommendation device of claim 6, wherein the training process of the implicit Dirichlet allocation model is as follows:
Generating a content sequence of the first and second content and a tag sequence of the at least one first and at least one second tag for a plurality of predetermined periods of time;
Inputting the content sequence and the label sequence as implicit Dirichlet allocation model training data, the first content and the second content as training samples, and the at least one first label and the at least one second label as basic units of a model;
Setting one or more implicit themes for training; and
And obtaining the topic similarity of each label in the at least one first label and the at least one second label and each implied topic.
9. the content recommendation device of claim 8, wherein the similarity calculation unit is to:
Multiplying the topic similarity of each of the at least one first label to each underlying topic by the topic similarity of each of the at least one second label to the underlying topic; and
Summing products of the first label and the second label for the same underlying topic to obtain a label similarity of the first label and the second label.
10. The content recommendation apparatus according to any one of claims 6 to 9,
The first content and the second content are video content;
The first content is one of user production content, professional production content, and brand production content; and
The second content is another one of user production content, professional production content, and brand production content.
11. An electronic device, comprising:
A processor; and
A memory having stored therein computer program instructions which, when executed by the processor, cause the processor to perform the content recommendation method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810258813.9A CN110555135B (en) | 2018-03-27 | 2018-03-27 | Content recommendation method, content recommendation device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810258813.9A CN110555135B (en) | 2018-03-27 | 2018-03-27 | Content recommendation method, content recommendation device and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110555135A true CN110555135A (en) | 2019-12-10 |
| CN110555135B CN110555135B (en) | 2023-04-07 |
Family
ID=68733788
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810258813.9A Active CN110555135B (en) | 2018-03-27 | 2018-03-27 | Content recommendation method, content recommendation device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110555135B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113590851A (en) * | 2020-05-01 | 2021-11-02 | 脸谱公司 | Suggesting entities in an online system to create content and add tags to the content |
| CN114625779A (en) * | 2022-03-07 | 2022-06-14 | 深圳市虎瑞科技有限公司 | Method and system for intelligently recommending contents by intelligent large screen and electronic equipment |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1203315A1 (en) * | 1999-06-15 | 2002-05-08 | Kanisa Inc. | System and method for document management based on a plurality of knowledge taxonomies |
| WO2008033840A2 (en) * | 2006-09-12 | 2008-03-20 | Eyespot Corporation | System and methods for creating, collecting, and using metadata |
| WO2008073594A1 (en) * | 2006-12-09 | 2008-06-19 | Motorola, Inc. | A content recommendation system and a method of operation therefor |
| US20090089141A1 (en) * | 2007-09-27 | 2009-04-02 | Yahoo!, Inc. | Methods for cross-market brand advertising, content metric analysis, and placement recommendations |
| CN102047277A (en) * | 2008-05-29 | 2011-05-04 | 诺基亚公司 | Method, apparatus, and computer program product for content use assignment by exploiting social graph information |
| WO2011064675A1 (en) * | 2009-11-30 | 2011-06-03 | France Telecom | Method and system to recommend applications from an application market place |
| CN103177093A (en) * | 2013-03-13 | 2013-06-26 | 北京开心人信息技术有限公司 | General recommendation method and system based on object tags |
| CN103744849A (en) * | 2011-12-27 | 2014-04-23 | 北京奇虎科技有限公司 | Method and device for automatic recommendation application |
| US20140129373A1 (en) * | 2012-11-02 | 2014-05-08 | Ebay Inc. | Item recommendations based on true fit determination |
| CA2821177A1 (en) * | 2013-01-10 | 2014-07-10 | Spielo International Canada Ulc | Systems and methods for recommending games using distributed storage |
| CN104156472A (en) * | 2014-08-25 | 2014-11-19 | 四达时代通讯网络技术有限公司 | Video recommendation method and system |
| CN104598518A (en) * | 2014-12-10 | 2015-05-06 | 深圳市腾讯计算机系统有限公司 | Content pushing method and device |
| US20150199333A1 (en) * | 2014-01-15 | 2015-07-16 | Abbyy Infopoisk Llc | Automatic extraction of named entities from texts |
| US20160117329A1 (en) * | 2014-10-22 | 2016-04-28 | Legit Corporation | Systems and methods for social recommendations |
| CN105959374A (en) * | 2016-05-12 | 2016-09-21 | 腾讯科技（深圳）有限公司 | Data recommendation method and data recommendation equipment |
| CN106055617A (en) * | 2016-05-26 | 2016-10-26 | 乐视控股（北京）有限公司 | Data pushing method and device |
| CN106294502A (en) * | 2015-06-09 | 2017-01-04 | 北京搜狗科技发展有限公司 | A kind of e-book information processing method and processing device |
| CN106649848A (en) * | 2016-12-30 | 2017-05-10 | 合网络技术（北京）有限公司 | Video recommendation method and video recommendation device |
| CN106708829A (en) * | 2015-07-31 | 2017-05-24 | 腾讯科技（深圳）有限公司 | Data recommendation method and data recommendation system |
| CN106776503A (en) * | 2016-12-22 | 2017-05-31 | 东软集团股份有限公司 | The determination method and device of text semantic similarity |
| CN107733984A (en) * | 2017-09-14 | 2018-02-23 | 深圳市金立通信设备有限公司 | A kind of method, terminal and computer-readable recording medium for pushing screen locking information |
-
2018
- 2018-03-27 CN CN201810258813.9A patent/CN110555135B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1203315A1 (en) * | 1999-06-15 | 2002-05-08 | Kanisa Inc. | System and method for document management based on a plurality of knowledge taxonomies |
| WO2008033840A2 (en) * | 2006-09-12 | 2008-03-20 | Eyespot Corporation | System and methods for creating, collecting, and using metadata |
| WO2008073594A1 (en) * | 2006-12-09 | 2008-06-19 | Motorola, Inc. | A content recommendation system and a method of operation therefor |
| US20090089141A1 (en) * | 2007-09-27 | 2009-04-02 | Yahoo!, Inc. | Methods for cross-market brand advertising, content metric analysis, and placement recommendations |
| CN102047277A (en) * | 2008-05-29 | 2011-05-04 | 诺基亚公司 | Method, apparatus, and computer program product for content use assignment by exploiting social graph information |
| WO2011064675A1 (en) * | 2009-11-30 | 2011-06-03 | France Telecom | Method and system to recommend applications from an application market place |
| CN103744849A (en) * | 2011-12-27 | 2014-04-23 | 北京奇虎科技有限公司 | Method and device for automatic recommendation application |
| US20140129373A1 (en) * | 2012-11-02 | 2014-05-08 | Ebay Inc. | Item recommendations based on true fit determination |
| CA2821177A1 (en) * | 2013-01-10 | 2014-07-10 | Spielo International Canada Ulc | Systems and methods for recommending games using distributed storage |
| CN103177093A (en) * | 2013-03-13 | 2013-06-26 | 北京开心人信息技术有限公司 | General recommendation method and system based on object tags |
| US20150199333A1 (en) * | 2014-01-15 | 2015-07-16 | Abbyy Infopoisk Llc | Automatic extraction of named entities from texts |
| CN104156472A (en) * | 2014-08-25 | 2014-11-19 | 四达时代通讯网络技术有限公司 | Video recommendation method and system |
| US20160117329A1 (en) * | 2014-10-22 | 2016-04-28 | Legit Corporation | Systems and methods for social recommendations |
| CN104598518A (en) * | 2014-12-10 | 2015-05-06 | 深圳市腾讯计算机系统有限公司 | Content pushing method and device |
| CN106294502A (en) * | 2015-06-09 | 2017-01-04 | 北京搜狗科技发展有限公司 | A kind of e-book information processing method and processing device |
| CN106708829A (en) * | 2015-07-31 | 2017-05-24 | 腾讯科技（深圳）有限公司 | Data recommendation method and data recommendation system |
| CN105959374A (en) * | 2016-05-12 | 2016-09-21 | 腾讯科技（深圳）有限公司 | Data recommendation method and data recommendation equipment |
| CN106055617A (en) * | 2016-05-26 | 2016-10-26 | 乐视控股（北京）有限公司 | Data pushing method and device |
| CN106776503A (en) * | 2016-12-22 | 2017-05-31 | 东软集团股份有限公司 | The determination method and device of text semantic similarity |
| CN106649848A (en) * | 2016-12-30 | 2017-05-10 | 合网络技术（北京）有限公司 | Video recommendation method and video recommendation device |
| CN107733984A (en) * | 2017-09-14 | 2018-02-23 | 深圳市金立通信设备有限公司 | A kind of method, terminal and computer-readable recording medium for pushing screen locking information |
Non-Patent Citations (3)
| Title |
|---|
| 宋美娜;赵雪君;鄂海红;: "基于分类的多属性实体推荐" * |
| 朱征宇;张小林;熊茜;谢祈鸿;: "基于用户兴趣子类的协作推荐算法" * |
| 游贵荣;陈杰;: "一种高校图书馆新书个性化推荐方法" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113590851A (en) * | 2020-05-01 | 2021-11-02 | 脸谱公司 | Suggesting entities in an online system to create content and add tags to the content |
| CN114625779A (en) * | 2022-03-07 | 2022-06-14 | 深圳市虎瑞科技有限公司 | Method and system for intelligently recommending contents by intelligent large screen and electronic equipment |
| CN114625779B (en) * | 2022-03-07 | 2024-04-26 | 上海合志信息技术有限公司 | Method, system and electronic equipment for intelligent recommendation of content by intelligent large screen |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110555135B (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110378732B (en) | Information display method, information association method, device, equipment and storage medium | |
| US11455465B2 (en) | Book analysis and recommendation | |
| US20190114687A1 (en) | Global Vector Recommendations Based on Implicit Interaction and Profile Data | |
| US20170235830A1 (en) | Adjusting Sentiment Scoring For Online Content Using Baseline Attitude of Content Author | |
| US20190102374A1 (en) | Predicting future trending topics | |
| US9202142B1 (en) | Automatic assessment of books to determine suitability for audio book conversion | |
| US20140330760A1 (en) | Content distribution | |
| CN105095508A (en) | Multimedia content recommendation method and multimedia content recommendation apparatus | |
| CN114896454B (en) | Short video data recommendation method and system based on label analysis | |
| CN109255037B (en) | Method and apparatus for outputting information | |
| CN110309414B (en) | Content recommendation method, content recommendation device and electronic equipment | |
| US11748797B2 (en) | System and method for providing recommendations to a target user based upon review and ratings data | |
| CN113688310B (en) | Content recommendation method, device, equipment and storage medium | |
| CN110019948B (en) | Method and apparatus for outputting information | |
| CN107332905A (en) | Information-pushing method, device and server | |
| CN107515870B (en) | Searching method and device and searching device | |
| CN116821475A (en) | Video recommendation method and device based on client data and computer equipment | |
| US20090327877A1 (en) | System and method for disambiguating text labeling content objects | |
| CN113590851A (en) | Suggesting entities in an online system to create content and add tags to the content | |
| CN110555135B (en) | Content recommendation method, content recommendation device and electronic equipment | |
| Araújo et al. | Tensorcast: forecasting time-evolving networks with contextual information | |
| CN112948602B (en) | Content display method, device, system, equipment and storage medium | |
| EP3374879A1 (en) | Provide interactive content generation for document | |
| CN110555131B (en) | Content recommendation method, content recommendation device and electronic equipment | |
| CN107483595B (en) | Information pushing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200513 Address after: 310052 room 508, floor 5, building 4, No. 699, Wangshang Road, Changhe street, Binjiang District, Hangzhou City, Zhejiang Province Applicant after: Alibaba (China) Co.,Ltd. Address before: 100080 Beijing Haidian District city Haidian street A Sinosteel International Plaza No. 8 block 5 layer A, C Applicant before: Youku network technology (Beijing) Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |