文章基本信息

标题：Solving the apparent diversity-accuracy dilemma of recommender systems
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作者：Tao Zhou ; Zoltán Kuscsik ; Jian-Guo Liu 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2010
卷号：107
期号：10
页码：4511-4515
DOI：10.1073/pnas.1000488107
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Recommender systems use data on past user preferences to predict possible future likes 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