A NEW ALGORITHM FOR MULTI-MODE RECOMMENDATIONS IN SOCIAL TAGGING SYSTEMS

摘要：

　　Social tagging is one of the most important characteristics of web 2.0 services.Different from traditional recommendation algorithms,in social tagging systems,recommendation algorithms involve the ternary relations between users,items and tags.And algorithms that support integrated multi-mode recommendations are very appealing.We propose a multi-mode recommendation algorithm based on higher-order singular value decomposition,and our algorithm handles not only the existing triplets user,item,tag,but also the pairs user,item with no tags in social tagging system.Meanwhile.We propose a measure for user recommendations.We empirically show that our algorithm outperforms a state-of-the-art algorithm for multi-mode recommendations with a Last.fm dataset.

关键词： Social tagging systems Multi-recommendation Tensor factorization

作者: Tan Yang Yidong Cui Yuehui Jin Maoqiang Song

作者单位: State Key Laboratory of Network and Switching Technology,Beijing University of Posts and Telecommuni School of Software Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,Chi

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 915-919

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）