Integrating heterogeneous information via flexible regularization framework for recommendation

doi:10.1007/s10115-016-0925-0

	Integrating heterogeneous information via flexible regularization framework for recommendation
	Shi, Chuan 1; Liu, Jian 1; Zhuang, Fuzhen 2; Yu, Philip S.3; Wu, Bin 1
	2016-12-01
发表期刊	KNOWLEDGE AND INFORMATION SYSTEMS
ISSN	0219-1377
卷号	49 期号:3 页码:835-859
摘要	Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are very sparse or absent. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit this attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommender system as a heterogeneous information network and introduce meta-path-based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization-based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting users' and items' similarities as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impacts on users and items.
关键词	Recommender system Heterogeneous information network Matrix factorization Similarity measure
DOI	10.1007/s10115-016-0925-0
收录类别	SCI
语种	英语
资助项目	National Key Basic Research and Department (973) Program of China[2013CB329606] ; National Natural Science Foundation of China[71231002] ; National Natural Science Foundation of China[61473273] ; CCF-Tencent Open Fund ; Co-construction Project of Beijing Municipal Commission of Education ; US NSF[III-1526499] ; Microsoft Research Asia Collaborative Research Program
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000386120000002
出版者	SPRINGER LONDON LTD
引用统计	被引频次：46[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8021
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhuang, Fuzhen
作者单位	1.Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing, Peoples R China 2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing, Peoples R China 3.Univ Illinois, Comp Sci, Chicago, IL USA
推荐引用方式 GB/T 7714	Shi, Chuan,Liu, Jian,Zhuang, Fuzhen,et al. Integrating heterogeneous information via flexible regularization framework for recommendation[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2016,49(3):835-859.
APA	Shi, Chuan,Liu, Jian,Zhuang, Fuzhen,Yu, Philip S.,&Wu, Bin.(2016).Integrating heterogeneous information via flexible regularization framework for recommendation.KNOWLEDGE AND INFORMATION SYSTEMS,49(3),835-859.
MLA	Shi, Chuan,et al."Integrating heterogeneous information via flexible regularization framework for recommendation".KNOWLEDGE AND INFORMATION SYSTEMS 49.3(2016):835-859.