A Friend Recommendation Algorithm Based on Multiple factors in LBSNs

摘要：

　　In location-based social networks, the current friend recommendation algorithms just take a relatively single factor into account without comprehensive evaluations.To solve this problem, we design a framework-Multiple Heterogeneous Social Network (MHSN) according to users profiles, check-in records and interests.Based on this framework, we propose a friend recommendation model which consider multiple factors,including 1) a detecting model based on interest similarity by using users check-in records; 2) a social distance calculation method based on users social relationship; 3) a clustering method based on users check-in location information to measure the similarity among clusters.The top-k friends who satisfy the above conditions will be recommended to the target users.We evaluated our method using Foursquare data-sets and the results showed that our friend recommendation algorithm is more feasible and effective.

关键词： Multiple Heterogeneous Social Network Similar Interests Social Distance Space Distance Friend Recommendation

作者: Tiancheng Zhang Wei Wang Xiju Liao Dejun Yue Ge Yu

作者单位: Institute of information science and engineering 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: 31-36

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）