Friend Recommendation Algorithm Based on User Activity and Social Trust in LBSNs

摘要：

　　In LBSNs (Location-based Social Networks),friend recommendation results are mainly decided by the number of common friends or depending on similar user preferences.However, lack of description of semantic information about user activity preferences, insufficiency in building social trust among user relationships and individual score ranking by a crowd or the person from third party of social networks make recommendation quality undesirable.Aiming at this issue, FRBTA algorithm is proposed in this paper to recommend best friends by considering multiple factors such as user semantic activity preferences, social trust.Experimental results show that the proposed algorithm is feasible and effective.

关键词： LBSNs Activity Similarity Social Trust Friend Recommendation

作者: Chengcheng Su Yaxin Yu Mingfei Sui Haijun Zhang

作者单位: Northeastern University Shenyang, China

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 15-20

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）