Information push channel recommendation method based on content attribute and audience feature fusion

By analyzing the sentiment and content features of information texts and combining them with channel interest center vectors, recommendation scores are dynamically calculated, solving the problem of inaccurate matching of information dissemination channels and improving the accuracy and efficiency of information dissemination, as well as optimizing adaptability and recommendation performance.

CN118760803BActive Publication Date: 2026-07-24UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Application Number
CN202410746737.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-06-11
Publication Date
2026-07-24
Estimated Expiration
2044-06-11

AI Technical Summary

Technical Problem

Existing information recommendation systems lack systematic algorithms that can dynamically adapt to user preferences, channel characteristics, and information types in the context of social networks and internet media, resulting in inaccurate matching of information dissemination channels.

Method used

By analyzing the sentiment and content characteristics of information texts, a set of channel interest center vectors is constructed. Combining Euclidean distance and weight parameters, the recommendation score of information push channels is dynamically calculated, integrating content matching and audience matching recommendation scores.

Benefits of technology

It improved the accuracy and efficiency of information dissemination, optimized information dissemination strategies, enhanced user participation and information influence, and demonstrated excellent adaptability and recommendation performance.

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Abstract

The application discloses a method for recommending information pushing channels based on the fusion of content attributes and audience characteristics, which comprises the following steps: S1, analyzing the emotional tendency and content characteristics of information text to determine the content matching recommendation score of the first recommended pushing channel; S2, determining the audience matching recommendation score of the second recommended pushing channel according to the Euclidean distance between the news keyword vector and the channel interest center vector of each pushing channel; S3, calculating the recommendation score according to the content matching recommendation score and the audience matching recommendation score to determine the final recommended pushing channel. The application aims to improve the accuracy and efficiency of news media selection and information dissemination channel recommendation, dynamically adapting to the changing media environment and audience habits by using advanced natural language processing technology and user behavior analysis. This innovative method provides an intelligent tool for media publishing, digital marketing and social media management to effectively improve information influence and resource use efficiency.
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Citation Information

Patent Citations

  • Content pushing method and device, computing equipment and computer readable storage medium

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  • Network forum user interest recommendation algorithm based on text features and emotional tendencies

    CN115510326A