Time-aware video-on-demand recommendation method for TV boxes

A technology for TV boxes and recommendation methods, applied in electrical components, image communication, selective content distribution, etc., to achieve optimal recommendation accuracy, improved video recommendation effects, and accurate and personalized recommendation services

Active Publication Date: 2019-01-15
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that the on-demand video recommendation module in the network TV box cannot adaptively adjust the video recommendation strategy in a time-sensitive manner, in order to provide users with more personalized recommendation services, increase user viscosity, and improve user experience, the present invention proposes a time-based Perception-oriented video-on-demand recommendation method for TV boxes, the technical solution adopted by the present invention is:

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Embodiment Construction

[0041] The present invention will be further elaborated below in conjunction with the accompanying drawings and embodiments. Among them, the accompanying drawings are only for illustrative purposes, showing only a schematic diagram or one of the realizable physical diagrams, rather than a fixed physical diagram, and should not be understood as a limitation to this patent; in order to better illustrate the present invention Some components in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some known structures and their descriptions in the drawings may be omitted.

[0042] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0043] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0044] The technical solutions of the ...

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Abstract

The invention relates to a time-aware video-on-demand recommendation method for TV boxes. According to the invention, based on a weight regression matrix decomposition model, the characteristics of time perception are introduced as auxiliary information to generate a recommendation list with time-varying attributes for time slots with similar playback modes. The invention aims at the on-demand video of the network television box, and the video recommendation effect can be remarkably improved after the invention is used; In addition, the invention can maintain better recommendation accuracy compared with the comparison algorithm during the cold start period of the video. In summary, the invention can bring more accurate and personalized recommendation service for the user of the TV box using the on-demand service.

Description

technical field [0001] The invention belongs to the field of recommendation systems and multimedia applications, and in particular relates to a novel method for recommending video on demand for TV boxes based on time perception. Background technique [0002] The emergence and development of the Internet has brought a lot of information to ordinary users, and at the same time it will bring about the problem of information overload. The introduction of a recommendation system is a common method to solve the problem of information overload. The recommendation system can calculate the user's personal preference and recommend personalized items to the user based on the user's personal information, item metadata, and interaction information between the user and the item. [0003] Compared with search engines, recommendation systems can actively provide users with personalized candidate items, instead of requiring users to initiate requests like search engines. In this mode, recomm...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N21/25H04N21/466H04N21/262H04N21/482
CPCH04N21/251H04N21/26258H04N21/4667H04N21/4668H04N21/4826
Inventor 吴迪吴展鹏
Owner SUN YAT SEN UNIV
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