CLiMF:Collaborative Less-Is-More Filtering

摘要：

　　In this paper we tackle the problem of recommendation in the scenarios with binary relevance data,when only a few (k) items are recommended to individual users.Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets,or not specifically focused on improving top-k recommendations.To solve the problem we propose a new CF approach,Collaborative Less-is-More Filtering (CLiMF).In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR),which is a well-known information retrieval metric for capturing the performance of top-k recommendations.We achieve linear computational complexity by introducing a lower bound of the smoothed reciprocal rank metric.Experiments on two social network datasets show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods.

作者: Yue Shi Alexandros Karatzoglou Linas Baltrunas Martha Larson Nuria Oliver Alan Hanjalic

作者单位: Delft University of Technology Telefonica Research

会议类型: 国际会议

会议名称: 2013年第23届人工智能国际会议(IJCAI-2013)

会议地点: 北京

会议语种:英文

页码: 3077-3081

在线出版日期: 2013-08-01（万方平台首次上网日期，不代表论文的发表时间）