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Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

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Abstract

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.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Fu-Zhen Zhuang, Ying-Min Zhou, Xiang Ao & Qing He

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Fu-Zhen Zhuang, Ying-Min Zhou, Xiang Ao & Qing He

  3. School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310027, China

    Hao-Chao Ying

  4. Meituan-Dianping Group, Beijing, 100102, China

    Fu-Zheng Zhang

  5. Microsoft Research Asia, Beijing, 100080, China

    Xing Xie

  6. Department of Management Science and Information Systems, Rutgers University, New Jersey, 07102, U.S.A.

    Hui Xiong

Authors
  1. Fu-Zhen Zhuang
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  2. Ying-Min Zhou
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  3. Hao-Chao Ying
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  4. Fu-Zheng Zhang
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  7. Qing He
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  8. Hui Xiong
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Correspondence to Hao-Chao Ying.

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Zhuang, FZ., Zhou, YM., Ying, HC. et al. Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining. J. Comput. Sci. Technol. 35, 305–319 (2020). https://doi.org/10.1007/s11390-020-9945-z

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  • DOI: https://doi.org/10.1007/s11390-020-9945-z

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