A social network short text recommendation method based on a word meaning topic model

Abstract

The invention relates to a social network short text recommendation method based on a word meaning topic model, which comprises the following specific steps of fusing the word representation learningbased on context attention mechanism of word meaning and synonym information into social network short text recommendation so as to enrich word level characteristics of a text; fusing the dirichlet multi-item mixed distribution short text topic modeling based on word meaning representation into social network short text recommendation so as to enrich text level characteristics; modeling the potential interest degree and tendency degree of the user evolved along with time by combining the social network user relationship, the short text topic characteristics of the user-related text based on word meaning representation and the potential relationship characteristics between the user and the text; predicting the potential tendency of the user to the text through a parameter estimation method,and selecting the text with the maximum tendency to recommend to the user to realize short text recommendation. According to the method, the word meaning information is fused into the short text topic modeling and the social network short text recommendation task, so that the accuracy of the social network short text recommendation task is improved.

Description

technical field [0001] The present invention relates to the technical field of social network recommendation technology and short text feature extraction, in particular to a social network short text recommendation method. Background technique [0002] In the recommendation field, a "recommendation system" is a system that recommends different content to different users based on user historical data, such as articles, friends, products or advertisements. Therefore, the system can often effectively extract personalized and customized information that is valuable to users from exponentially growing massive data. The recommendation systems of social networks are mostly based on user recommendations, and the content published by the same user is also diverse. Not every content is the user's concern. Therefore, text-based recommendations can better help users filter the information they care about. , so as to realize the precise delivery of text information such as article push ...

AI Technical Summary

Problems solved by technology

[0009] In order to solve the above-mentioned problems, the present invention provides a social network short text recommendation method based on a word meaning topic model to improve the accuracy of short text recommendation and solve the problem of difficulty in short text topic extraction

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