Time-factor-based group recommender algorithm on social networks

摘要：

　　The existing recommender systems are most aimed at single users,rather than groups.A group in social networks is characterized by complexity and diversity,so the traditional group recommender algorithms(GRAs)are unable to adapt to social networks.Therefore,we proposed a time-factor-based hybrid GRA for personalized Weibo recommendation on social networks.The new algorithm comprehensively considers the between-user interaction and determines the importance degree of a microblog through extraction of current context information.Moreover,it considers the between-group relation structure and interests also change with time.Here the real data from Sina Weibo were used in test and evaluation.The new algorithm outperforms the existing algorithms in Weibo group recommendation.

关键词： Social Network group recommendation Time-hybrid group Recommendation social relationship factor similarity factor

作者: ZUFENG ZHONG YAOQING DUAN

作者单位: Department of Information Management,Central China Normal University,Wuhan 430079,China

会议类型: 国内会议

会议名称: 第十三届海峡两岸图书资讯学学术研讨会

会议地点: 武汉

会议语种:英文

页码: 1-11

在线出版日期: 2016-07-01（万方平台首次上网日期，不代表论文的发表时间）