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Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

文献类型：期刊论文

作者Zhuang, Fu-Zhen1,2; Zhou, Ying-Min1,2; Ying, Hao-Chao3; Zhang, Fu-Zheng4; Ao, Xiang1,2; Xie, Xing5; He, Qing1,2; Xiong, Hui6
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2020-03-01
卷号35期号:2页码:305-319
关键词sequential recommendation novelty-seeking trait transfer learning
ISSN号1000-9000
DOI10.1007/s11390-020-9945-z
英文摘要Transfer learning has attracted a large amount of interest and research in last decades, and some effort has been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, almost all existing transfer learning work does not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario by mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performed on only one single target domain may not fully characterize users' novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we propose a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain datasets crawled from Douban to demonstrate the effectiveness of our proposed model. Moreover, we analyze the directed influence of the temporal property at the source and target domains in detail.
资助项目National Key Research and Development Program of China[2018YFB1004300] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[U1811461] ; National Natural Science Foundation of China[61773361] ; Project of Youth Innovation Promotion Association of Chinese Academy of Sciences[2017146]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000534804000008
出版者SCIENCE PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/15340]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ying, Hao-Chao
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Zhejiang Univ, Sch Med, Sch Publ Hlth, Hangzhou 310027, Peoples R China
4.Meituan Dianping Grp, Beijing 100102, Peoples R China
5.Microsoft Res Asia, Beijing 100080, Peoples R China
6.Rutgers State Univ, Dept Management Sci & Informat Syst, Newark, NJ 07102 USA
推荐引用方式
GB/T 7714		 Zhuang, Fu-Zhen,Zhou, Ying-Min,Ying, Hao-Chao,et al. Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2020,35(2):305-319.
APA Zhuang, Fu-Zhen.,Zhou, Ying-Min.,Ying, Hao-Chao.,Zhang, Fu-Zheng.,Ao, Xiang.,...&Xiong, Hui.(2020).Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,35(2),305-319.
MLA Zhuang, Fu-Zhen,et al."Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 35.2(2020):305-319.

入库方式： OAI收割

来源：计算技术研究所

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