A framework for diversifying recommendation lists by user interest expansion
Zhang, Zhu1; Zheng, Xiaolong1; Zeng, Daniel Dajun1,2
2016-08-01
发表期刊KNOWLEDGE-BASED SYSTEMS
卷号105期号:1页码:83-95
文章类型Article
摘要Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users' interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users' preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation. (C) 2016 Elsevier B.V. All rights reserved.
关键词Recommender Systems Collaborative Filtering Diversity Interest Expansion Social Tagging System
WOS标题词Science & Technology ; Technology
DOI10.1016/j.knosys.2016.05.010
关键词[WOS]SYSTEMS ; TAG
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(71472175 ; National Institutes of Health (NIH) of the USA(1R01DA037378-01) ; Ministry of Health of China(2012ZX10004801 ; 71103180 ; 2013ZX10004218) ; 71025001 ; 61175040)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000378961200008
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12149
专题复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
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Zhang, Zhu,Zheng, Xiaolong,Zeng, Daniel Dajun. A framework for diversifying recommendation lists by user interest expansion[J]. KNOWLEDGE-BASED SYSTEMS,2016,105(1):83-95.
APA Zhang, Zhu,Zheng, Xiaolong,&Zeng, Daniel Dajun.(2016).A framework for diversifying recommendation lists by user interest expansion.KNOWLEDGE-BASED SYSTEMS,105(1),83-95.
MLA Zhang, Zhu,et al."A framework for diversifying recommendation lists by user interest expansion".KNOWLEDGE-BASED SYSTEMS 105.1(2016):83-95.
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