A Clustering-based Similarity Measurement for Collaborative Filtering

摘要：

　　Similarity measurement is a crucial process in collaborative filtering.User similarity is computed solely based on the numerical ratings of users.In this paper,we argue that the social information of users should be also taken into consideration to improve the performance of traditional similarity measurements.To achieve this,we propose a clustering-based similarity measurement approach incorporating user social information.In order to cluster the users effectively,we propose a novel distance metric based on taxonomy tree which can easily process the numerical and categorical information of users.Meanwhile,we also address how to determine the contribution of different types of information in the distance metric.After clustering the users,we introduce the incorporating strategy of our proposed similarity measurement.We perform a series of experiments on a real world dataset and compare the performance of our approach against that of traditional approaches.Experiments demonstrate that the proposed approach considerably outperforms the traditional approaches.

关键词： similarity clustering social information collaborative filtering recommendation system

作者: Liang Gu Peng Yang Yongqiang Dong

作者单位: School of Computer Science and Engineering,Southeast University,Nanjing 211189,China Key Laboratory of Computer Network and Information Integration(Southeast University),Ministry of Edu

会议类型: 国际会议

会议名称: 2014 2nd International Conference on Advanced Cloud and Big Data (CBD 2014)(2014年先进云计算和大数据国际会议)

会议地点: 安徽黄山

会议语种:英文

页码: 282-287

在线出版日期: 2014-11-20（万方平台首次上网日期，不代表论文的发表时间）