基于维基百科类别图的推特用户兴趣挖掘

Mining User Interests on Twitter Using Wikipedia Category Graph

以Twitter为代表的社交网络在人们的生活中发挥着重要作用,其庞大的用户群体给社交网络数据挖掘带来了巨大的价值。社交网络用户兴趣建模方法被广泛研究,并被用于提供个性化推荐。文中提出了一种基于维基百科类别图的Twitter用户兴趣挖掘和表示方法。首先,该方法根据用户活跃度的差异,分别采用基于推文内容的方法和基于关注账号信息的方法来实现活跃用户与非活跃用户的兴趣挖掘。然后,在维基百科类别图上使用个性化PageRank算法进一步拓展用户兴趣,生成维基百科类别表示的用户兴趣画像。在推文推荐的应用背景下,对用户兴趣建模策略进行了实验分析和比较。实验结果表明,与现有的Twitter用户兴趣挖掘方法相比,所提方法显著提升了推文推荐效果,能够有效地改进用户兴趣挖掘效果。

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.