CN107948755B - Video content recommendation method and system combining user watching duration - Google Patents
Video content recommendation method and system combining user watching duration Download PDFInfo
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- CN107948755B CN107948755B CN201711277065.0A CN201711277065A CN107948755B CN 107948755 B CN107948755 B CN 107948755B CN 201711277065 A CN201711277065 A CN 201711277065A CN 107948755 B CN107948755 B CN 107948755B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/251—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/252—Processing of multiple end-users' preferences to derive collaborative data
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
- H04N21/25866—Management of end-user data
- H04N21/25891—Management of end-user data being end-user preferences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/4508—Management of client data or end-user data
- H04N21/4532—Management of client data or end-user data involving end-user characteristics, e.g. viewer profile, preferences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
Abstract
The invention discloses a video content recommendation method and system combining with user watching duration, and aims to solve the problem that in the prior art, the video recommendation mostly considers the click of a user and does not consider other user use factors, so that the judgment result is poor; according to the method, the user watching time length is used as an important factor of user preference to identify the users of the same type, the watching programs of the users of the same type are recommended, the subjective action of people is exerted, and the recommendation result has better accuracy; the method and the device are suitable for the content recommendation related field.
Description
Technical Field
The invention relates to a video content recommendation method, in particular to a video content recommendation method and system combining watching duration of a user.
Background
With the development of OTT boxes and smart televisions, internet videos are increasingly abundant, and some users face the difficulty of finding favorite contents. Many recommendation methods based on different latitudes of program labels, contents, etc. have appeared, but these methods neglect subjective initiative of people, and recommended contents may not be liked and desired to be viewed by the user, or only the number of times of recording of contents viewed by the user is used without considering the duration of the contents viewed by the user.
The invention provides a new method and a system, which are used for analyzing the time length of historical content watched by a user, counting the watching time of the user, recommending the historical content watched by the same type of user to the user who does not watch the historical content by classifying the users with the same content and similar watching time length into one class, and recommending the content watched by the user with a certain watching target to the user who does not have a definite target.
Disclosure of Invention
The invention aims to: aiming at the problem that in the prior art, most of video recommendation considers the click of a user and does not consider other user use factors to cause poor judgment result, the application provides a video content recommendation method and system combining the watching duration of the user.
The technical scheme adopted by the invention is as follows:
a video content recommendation method combining with user watching duration comprises the following steps:
step 1: recording m contents P1, P2, P3 and P4 … Pm viewed in n days in user and recording as Tn(Pm)，Tn(Pm) The time length corresponding to m contents viewed in the past n days is represented by sim (a) MAX (T)n(Px) SIM) represents the longest content P used by the user a in the past n daysxThe length of time of;
step 2: directly dividing the user group to obtain T in the user group an(Px) If T isn(Px) Absence, exclusion of PxAnd m is m-1, and the step 1 is returned;
and step 3: obtaining a user group b which is close to the SIM (A) value of the user group a;
and 4, step 4: finding out user B in user group B according to SIM (A) value in step 3, so that Tn(Px) Is that the user B is to the content PxThe longest viewing time of;
and 5: if the viewing record contained in the history of the user B is not in the history of the user A, recommending the history of the user B to the user A, and if not, excluding the user B and returning to the step 3;
a video content recommendation system combining the watching duration of a user comprises a user history watching record acquisition module, a user similarity calculation module and a content program recommendation module;
the user history viewing record acquisition module: recording the watching content of the user;
the user similarity calculation module: respectively calculating the time length of the user group a for watching m contents in the past n days, finding out the content P with the longest watching time, calculating the time length of each user in the user group a for watching the content P, finding out the user group B with the time length similar to the time length of the user A for watching the content P, calculating the time length of each user in the user group B for watching the content, sequencing each user from large to small according to the time length for watching the content, and finding out one user B for enabling the time length for watching the content P to be arranged at the top;
a content program recommendation module: the content watched by the user group b and the content not watched by the user group a is recommended to the user a.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the method, the user watching time length is used as an important factor of user preference to identify the users of the same type, the watching programs of the users of the same type are recommended, the subjective action of people is exerted, and the recommendation result has better accuracy;
2. according to the method and the device, the watching duration of the user is also considered while the user watching content of the user is considered, so that more accurate user watching preference evaluation is obtained, and the content recommendation is more in line with the user preference.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts. The drawings are not intended to be to scale as practical, emphasis instead being placed upon illustrating the principles of the invention.
FIG. 1 is a flow chart of a content recommendation method of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
A video content recommendation method combining with user watching duration comprises the following steps:
step 1: recording m contents P1, P2, P3 and P4 … Pm viewed and recorded in n days of a user, wherein Tn (Pm) represents the time length corresponding to the m contents viewed in the past n days, and SIM (A) is set to MAX (Tn (Px)), and the SIM represents the time length of the longest content Px viewed by the user A in the past n days;
step 2: directly dividing the user group to obtain Tn (Px) in the user group a, if Tn (Px) does not exist, removing Px, and returning to the step 1, wherein m is m-1;
and step 3: obtaining a user group b which is close to the SIM (A) value of the user group a;
and 4, step 4: finding out a user B in the user group B according to the SIM (A) value in the step 3, wherein Tn (Px) is the longest watching time of the user B on the content Px;
and 5: if the viewing record contained in the history of the user B is not in the history of the user A, recommending the history of the user B to the user A, and if not, excluding the user B and returning to the step 3;
a video content recommendation system combining the watching duration of a user comprises a user history watching record acquisition module, a user similarity calculation module and a content program recommendation module;
the user history viewing record acquisition module: recording the watching content of the user;
the user similarity calculation module: respectively calculating the time length of the user group a for watching m contents in the past n days, finding out the content P with the longest watching time, calculating the time length of each user in the user group a for watching the content P, finding out the user group B with the time length similar to the time length of the user A for watching the content P, calculating the time length of each user in the user group B for watching the content, sequencing each user from large to small according to the time length for watching the content, and finding out one user B for enabling the time length for watching the content P to be arranged at the top;
a content program recommendation module: the content watched by the user group b and the content not watched by the user group a is recommended to the user a.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (2)
1. A video content recommendation method combining with user watching duration is characterized by comprising the following steps:
step 1: recording m contents P1, P2, P3 and P4 … Pm viewed in n days in user and recording as Tn(Pm)，Tn(Pm) The time length corresponding to m contents viewed in the past n days is represented by sim (a) MAX (T)n(Px) SIM) represents the longest content P used by the user a in the past n daysxThe length of time of;
step 2: directly dividing the user group to obtain T in the user group an(Px) If T isn(Px) Absence, exclusion of PxAnd m is m-1, and the step 1 is returned;
and step 3: obtaining a user group b which is close to the SIM (A) value of the user group a;
and 4, step 4: calculating the time length of the content watched by each user in the user group B, sequencing each user from large to small according to the time length of the watched content, and finding out a user B so that the time length of the watched content P is arranged in the front;
and 5: and if the viewing record contained in the history of the user B is not contained in the history of the user A, recommending the history of the user B to the user A, and if not, excluding the user B and returning to the step 3.
2. A video content recommendation system combining the watching duration of a user is characterized by comprising a user history watching record acquisition module, a user similarity calculation module and a content program recommendation module;
the user history viewing record acquisition module: recording the watching content of the user;
the user similarity calculation module: respectively calculating the time length of the user group a for watching m contents in the past n days, finding out the content P with the longest watching time, calculating the time length of each user in the user group a for watching the content P, and finding out the user group b which is similar to the time length of the user A for watching the content P;
a content program recommendation module: the content watched by the user group b and the content not watched by the user group a is recommended to the user a.
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| CN112102038A (en) * | 2020-09-16 | 2020-12-18 | 汤涛 | Optimization method for live broadcast e-commerce platform user access database based on big data |
| CN113949931A (en) * | 2021-10-13 | 2022-01-18 | 深圳市视壮科技有限公司 | IPTV program recommendation method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102917269A (en) * | 2012-09-29 | 2013-02-06 | 青岛海信电器股份有限公司 | Television program recommendation system and method |
| CN103209342A (en) * | 2013-04-01 | 2013-07-17 | 电子科技大学 | Collaborative filtering recommendation method introducing video popularity and user interest change |
| CN105808537A (en) * | 2014-12-29 | 2016-07-27 | Tcl集团股份有限公司 | A Storm-based real-time recommendation method and a system therefor |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102917269A (en) * | 2012-09-29 | 2013-02-06 | 青岛海信电器股份有限公司 | Television program recommendation system and method |
| CN103209342A (en) * | 2013-04-01 | 2013-07-17 | 电子科技大学 | Collaborative filtering recommendation method introducing video popularity and user interest change |
| CN105808537A (en) * | 2014-12-29 | 2016-07-27 | Tcl集团股份有限公司 | A Storm-based real-time recommendation method and a system therefor |
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