A framework for diversifying recommendation lists by user interest expansion

doi:10.1016/j.knosys.2016.05.010

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	A framework for diversifying recommendation lists by user interest expansion
	Zhang, Zhu1 ; Zheng, Xiaolong1 ; Zeng, Daniel Dajun1,2
发表期刊	KNOWLEDGE-BASED SYSTEMS
	2016-08-01
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
DOI	10.1016/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：10[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	多模态人工智能系统全国重点实验室_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
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	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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