关键词: 行为关系, 语义活动, 相似性计算, 物理位置, 最大似然估计, 关系推演

Abstract: Finding close relationships among users in social networks for recommendation services is one of hot research issues about social behavior relationships in both academic and industry domain. So far, most researches of behavior relationships in social networks are limited only to “follow” relationship, which results in the discovery of user relationships is inaccurate and incomplete. Fortunately, a special type of relationship supported by Twitter, i.e., “@(mention)” relationship, has been paid lots of attention for exploring latent reasonable behavior relationships due to its characteristics of revealing more closely relationships than the “follow” relationship. In this light, this paper proposes a new concept, UPBR (user pair behavior relationship). Two users producing similar semantic behaviors such as food, travel and shopping in some approximate geography space will have a UPBR relationship. Further, this paper also proposes an inferring model named UPBR-IM correspondingly for verifying UPBR. UPBR-IM infers not only activity similarity of semantic behaviors depending on “@” Tweets text, but also location similarity by means of maximum likelihood estimation method. Finally, this paper uses both similarities of activity and location to justify whether a pair of users have a UPBR relationship. The extensive experiment results validate the feasibility and effectiveness of the UPBR-IM model.

Key words: behavior relationship, semantic activity, similarity computing, geography position, maximum likelihood estimation, relationship inference