Multi-task television program recommendation method integrating user click behaviors as well as user interests and preferences

A TV program and user interest technology, applied in the field of multi-task TV program recommendation that integrates user click behavior and user interest preferences, can solve the problems of not considering whether to like TV programs, not being able to meet the user's personalized recommendation needs, etc.

Active Publication Date: 2020-07-07
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'

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  • Multi-task television program recommendation method integrating user click behaviors as well as user interests and preferences
  • Multi-task television program recommendation method integrating user click behaviors as well as user interests and preferences
  • Multi-task television program recommendation method integrating user click behaviors as well as user interests and preferences

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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 in conjunction with the accompanying drawings and specific embodiments. It should be noted here that the descriptions of these embodiments are used to help understand the present invention, but are not intended to limit 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 time for this program to the overall duration of the program as the user's interest preference for this program, which is divided into 5 rating levels, and the ratio is [0-0.2] for level 1, and the ratio Level 2 is at (0.2-0.4], level 3 is at the ratio...

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Abstract

The invention discloses a multi-task television program recommendation method integrating user click behaviors as well as user interests and preferences, which improves the television program recommendation effect through integrating user clicking and multi-task learning of user interests and preferences in television program recommendation. The multi-task television program recommendation methodcomprises the steps of: establishing a clicking model of television programs by a user from historical clicking program data of the user based on an attention network fusion neural network factorization algorithm; establishing an interest and preference model of the user for the programs from the user interest and preference data based on the attention network fusion neural network factorization algorithm; and finally, comprehensively considering the click behaviors of the user and the preference behaviors of the user during recommendation, so that the recommended television programs can better meet the requirements of the user, and the satisfaction degree of the user is improved.

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 that integrates 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 made in both industry and academia. 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. Furthe...

Claims

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Application Information

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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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