Social network user recommendation method based on extraction of user interest and social topic

Abstract

The invention discloses a social network user recommendation method based on the extraction of the user interest and the social topic. The method comprises the following steps: obtaining the ''follower-followee'' relationship and the ''mutual-following'' relationship information of a user in a social network; extracting the social topic and the interest topic of the user; on the basis of the topics, independently forming the social community and the interest community of the user; and independently tidying each community into a matrix form, using a matrix decomposition method to decompose each community matrix to calculate the intention following score of each user on each community matrix, taking the maximum value of the intention following scores of each user on all communities as a final intention following score, sorting the final intention following score between a target user and other users for the target user, and finally selecting the TOP-N users with the highest score as recommended following users. The method has the advantages that an accurate social network user recommendation result is obtained.

Description

technical field [0001] The present invention relates to a social network user recommendation technology, in particular to a social network user recommendation method based on user interests and social topic extraction. The recommendation method uses the UIS-LDA model method to extract user interest topics and social topics. Based on the extracted topics, the user's interest community and social community are respectively formed. Then, each community is matrix decomposed through the matrix decomposition algorithm to obtain the willingness to follow scores between each user pair. Finally, the score of the target user is calculated. Sort and obtain the N users who are willing to follow with the highest scores as recommended users. This method aims to find users with similar interests and social circles in social networks and recommend them to target users, thereby improving the accuracy of user recommendation. Background technique [0002] With the development of social networ...

AI Technical Summary

Problems solved by technology

[0004] However, the existing content-based recommendation methods completely rely on blog post content without considering the user's social factors; meanwhile, the existing collaborative filtering methods and topic model methods usually use the "follower-followee" relationship to describe the user's preferences, The "follower-followee" relationship itself can be established by the user's social or interest factors, so these methods do not distinguish the user's social and interest needs when following another person, resulting in inaccurate recommendation results

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