Solving the apparent diversity-accuracy dilemma of recommender systems

摘要：

Recommender systems use data on past user preferences to predict possible future tikes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.

关键词： hybrid algorithms information filtering heat diffusion bipartite networks personalization

作者: Tao Zhou Zoltan Kuscsik Jian-Guo Liu Joseph Rushton Wakeling Matus Medo Yi-Cheng Zhang

作者单位: Department of Physics, University of Fribourg, Chemin du Musee 3, CH-1700 Fribourg, Switzerland Depa Department of Physics, University of Fribourg, Chemin du Musee 3, CH-1700 Fribourg, Switzerland Oepa Department of Physics, University of Fribourg, Chemin du Musee 3, CH-1700 Fribourg, Switzerland Department of Physics, University of Fribourg, Chemin du Musee 3, CH-1700 Fribourg, Switzerland Rese

会议类型: 国际会议

会议名称: International Workshop on Statistical Physics and Computer Sciences(统计物理与计算机科学交叉研究国际研讨会 )

会议地点: 北京

会议语种:英文

页码: 321-325

在线出版日期: 2010-07-08（万方平台首次上网日期，不代表论文的发表时间）