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A Multi-task TV Program Recommendation Method Combining User Click Behavior and User Interest Preference

A TV program and interest preference technology, applied in the field of multi-task TV program recommendation that integrates user click behavior and user interest preference, can solve the problems of not considering whether to like TV programs, not being able to meet the user's personalized recommendation needs, etc., to achieve Improve the effect of recommendation, increase the diversity of recommendations, and improve the effect of robustness

Active Publication Date: 2022-06-17
ZHEJIANG UNIVERSITY OF MEDIA AND COMMUNICATIONS
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AI Technical Summary

Problems solved by technology

As the demand of TV users is getting higher and higher, the single-task recommendation system often only considers the user's click behavior, and does not consider whether the user likes the TV program after clicking, and the user's interest preference is particularly important in the recommendation process. , so the original recommendation system that only considered the user's click behavior is not able to meet the user's personalized recommendation needs

Method used

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  • A Multi-task TV Program Recommendation Method Combining User Click Behavior and User Interest Preference
  • A Multi-task TV Program Recommendation Method Combining User Click Behavior and User Interest Preference
  • A Multi-task TV Program Recommendation Method Combining User Click Behavior and User Interest Preference

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Embodiment Construction

[0051] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted here that the descriptions of these embodiments are used to help the understanding of the present invention, but do not constitute a limitation of the present invention.

[0052] A multi-task TV program recommendation method that integrates user click behavior and interest preferences, and is implemented through the python computer language, including the following steps:

[0053] Step 1: Data Preprocessing

[0054] The user's interest preference extraction method is based on the ratio of the user's viewing duration of the program and the overall duration of the program as the user's interest preference for this program, and is divided into 5 scoring levels, and the ratio is in [0-0.2]. Level 2 is at (0.2-0.4], level 3 is at (0.4-0.6], lev...

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Abstract

The invention discloses a multi-task TV program recommendation method that integrates user click behavior and user interest preference. In the TV program recommendation, multi-task learning that integrates user click and user interest preference is used to improve the TV program recommendation effect. The method includes: The attention network fusion neural network factorization algorithm establishes the user's click model for TV programs from the user's historical click program data; Finally, the user's click behavior and user's preference behavior are considered comprehensively when recommending, so that the recommended TV programs can better meet the needs of users and improve user satisfaction.

Description

technical field [0001] The invention relates to the field of multi-task TV program recommenders, in particular to a multi-task TV program recommendation method integrating user click behavior and user interest preference. Background technique [0002] With the development of the Internet and information technology, in order to solve the problem of information overload, the research and development of recommender systems has become a hot spot, and many achievements have been achieved in both industry and academia. Due to the increasing demands of TV users, single-task recommendation systems often only consider the user's click behavior, and do not consider whether the user likes the TV program after clicking, and the user's interest preference is particularly important in the process of recommendation. , so the original recommendation system that only considers the user's click behavior is no longer able to meet the user's personalized recommendation needs. Furthermore, with...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06N3/08G06N3/04
CPCG06F16/9535G06N3/08G06N3/045
Inventor 周怡洁沈学文俞定国
Owner ZHEJIANG UNIVERSITY OF MEDIA AND COMMUNICATIONS
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