Knowledge Commons of Institute of Automation,CAS
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 | |
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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