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Mixed personalized recommendation method

A recommendation method and hybrid recommendation technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve problems such as not fully meeting requirements, and achieve good interpretability and recommendation effects.

Inactive Publication Date: 2018-01-19
上海视畅信息科技有限公司
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  • Claims
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AI Technical Summary

Problems solved by technology

[0002] There are many recommendation algorithms in the recommendation system, and there are many mature algorithms like collaborative filtering, but in different application scenarios and based on different data applications, they cannot fully meet the needs

Method used

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Embodiment Construction

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0024] Such as figure 1 As shown, a hybrid personalized recommendation method, the hybrid personalized recommendation method includes the following steps:

[0025] Step S1: Obtaining recommended video metadata;

[0026] In practical applications, the acquisition of recommendable video metadata includes: acquiring Internet data, in-depth EPG data, and on-demand metadata to form a unified video metadata management library.

[0027] Step S2: Perform similar vid...

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Abstract

The invention discloses a mixed personalized recommendation method, which comprises the following steps that: obtaining film and television metadata capable of being recommended; carrying out similarfilm and television retrieval to obtain a candidate film and television; through sorting weight, carrying out similar sorting on candidate films and televisions; carrying out user behavior clustering,and generating a mixed recommendation candidate film and television according to a clustering result; and after all candidate films and televisions are sorted for multiple times, obtaining a recommendation result used for displaying. User behaviors are clustered to establish a user portrait, the user portrait obtained by clustering is used for generating the recommendation result, and personalized recommendation is carried out on the basis of the short-term watching historical behavior of the user and a hot broadcast film library, wherein films, television plays, variety shows and the like are mixed in the recommendation result. On the basis of content recommendation, the method is free from the influence of user-film incidence matrix sparseness problems, is explanatory and has a recommendation effect.

Description

technical field [0001] The invention relates to the field of television video, in particular to a hybrid personalized recommendation method. Background technique [0002] There are many recommendation algorithms in the recommendation system, and there are many mature algorithms like collaborative filtering, but they cannot fully meet the needs in different application scenarios and based on different data applications. When using videos from the entire network as recommended content, it is inevitable to encounter the problem of sparse user-video correlation matrix. Contents of the invention [0003] In view of the above-mentioned shortcomings existing at present, the present invention provides a hybrid personalized recommendation method, which can perform hybrid personalized recommendation based on the user's short-term viewing history behavior and popular movie library. [0004] In order to achieve the above object, embodiments of the present invention adopt the followin...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 王世欣沈婧王守军
Owner 上海视畅信息科技有限公司
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