Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (2): 305-319.doi: 10.1007/s11390-020-9945-z

• Special Section on Learning and Mining in Dynamic Environments • Previous Articles     Next Articles

Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

Fu-Zhen Zhuang1,2, Senior Member, CCF, Ying-Min Zhou1,2, Hao-Chao Ying3,*, Fu-Zheng Zhang4, Xiang Ao1,2, Xing Xie5, Distinguished Member, CCF, ACM, Qing He1,2, Senior Member, CCF, Hui Xiong6, Fellow, IEEE        

  1. 1 Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 School of Public Health, Zhejiang University School of Medicine, Hangzhou 310027, China;
    4 Meituan-Dianping Group, Beijing 100102, China;
    5 Microsoft Research Asia, Beijing 100080, China;
    6 Department of Management Science and Information Systems, Rutgers University, New Jersey 07102, U.S.A.
  • Received:2019-07-15 Revised:2020-01-21 Online:2020-03-05 Published:2020-03-18
  • Contact: Hao-Chao Ying
  • About author:Fu-Zhen Zhuang is an associate professor in the Institute of Computing Technology, Chinese Academy of Sciences, Beijing. His research interests include transfer learning, machine learning, data mining, multi-task learning and recommendation systems. He has published more than 80 papers in some prestigious refereed journals and conference proceedings, such as TKDE, IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning System, and so on.
  • Supported by:
    The work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004300, the National Natural Science Foundation of China under Grant Nos. U1836206, U1811461, 61773361, and the Project of Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No. 2017146.

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.

Key words: sequential recommendation; novelty-seeking trait; transfer learning;

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