基于维基百科类别图的推特用户兴趣挖掘

doi:10.11896/j.issn.1002-137X.2019.09.010

Abstract

Abstract: Social network such as Twitter plays an important role in life,and the huge number of users makes social network data mining valuable.User interest modeling on social networks has been studied widely,and is used to provide personalized recommendations.This paper proposed a novel user interest mining and representation approach based on Wikipedia Category Graph.User interest profile is represented as a wikipedia category vector.First,according to the degree of user’s activeness,an interest mining method based on tweets is proposed for active users,and another method based on names and descriptions of followees is proposed for passive users.Then,user interest is extended and genera-lized based on Wikipedia Category Graph by personalized PageRank algorithm,and user interest profile is represented by wikipedia categories.The proposed interest modeling strategy was evaluated in the context of a tweet recommendation system.The results shows that the proposed approach improves the quality of recommendation significantly compared with the state-of-the-art Twitter user interest modeling approachs,which means it can provide a more effective user interest profile.

Key words: Personalized PageRank, Social network, Tweets recommendation, User interest

CLC Number:

G633.67

LIU Xiao-jie, LV Xiao-qiang, WANG Xiao-ling, ZHANG Wei, ZHAO An. Mining User Interests on Twitter Using Wikipedia Category Graph[J].Computer Science, 2019, 46(9): 79-84.

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