New media content recommendation method and system

A recommendation method and media content technology, applied in the field of data processing, can solve problems such as inaccurate recommendations

Active Publication Date: 2020-02-07
北京速途网络科技股份有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Aiming at the deficiencies of the prior art, the present invention provides a new media content recommendation method, w...

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  • New media content recommendation method and system
  • New media content recommendation method and system
  • New media content recommendation method and system

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Embodiment Construction

[0088] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. example. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0089] The embodiment of the present application provides a new media content recommendation method and system, which solves the technical problem of inaccurate recommendation when the existing recommendation system is applied to new media content, and achieves highly accurate recommendation of interesting content for users.

[0090] The technical solution in the embodiment of the present application is to solve the above-mentioned...

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Abstract

The invention provides a new media content recommendation method and system, and relates to the field of data processing. According to the method, the text in the new media text database is represented by a semantic neural network; the method not only reflects the real text, but also reflects the knowledge most likely to be associated when people see the text, on one hand, the method is beneficialto introducing the prior knowledge, the information lacking in the text content is complemented by introducing the prior knowledge, on the other hand, the problem of natural language fuzziness can bealleviated, and the learning and reasoning capabilities of the algorithm are enhanced. The method is based on a semantic neural network. Construction of three-layer attention model, according to themethod, a three-layer attention model is improved by introducing external knowledge and a feedback mechanism to obtain a recommendation model, a deep learning algorithm is improved based on the recommendation model, intelligent recommendation of new media content is realized by applying the improved deep learning algorithm, the matching degree between users of the new media content interested by the users is improved, and accurate pushing is realized.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for recommending new media content. Background technique [0002] In the era of information overload, the task of the recommendation system is to connect users and information, help users find valuable information for themselves, and at the same time let the information be displayed in front of users who are interested in it, so as to realize the relationship between information consumers and information producers. Win-win. [0003] Existing recommendation systems generally adopt two methods: content-based recommendation method and collaborative filtering method. The basic idea of ​​content-based recommendation is to recommend similar content to users based on the content they browse or bookmark, as well as user preferences. Collaborative filtering methods are further divided into user-based collaborative filtering and model-based collaborative filtering. The ...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F16/33G06N3/04
CPCG06F16/9535G06F16/3344G06N3/044G06N3/045
Inventor 范锋
Owner 北京速途网络科技股份有限公司
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