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Regular Paper

Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Public Health, Zhejiang University School of Medicine, Hangzhou 310027, China
Meituan-Dianping Group, Beijing 100102, China
Microsoft Research Asia, Beijing 100080, China
Department of Management Science and Information Systems, Rutgers University, New Jersey 07102, U.S.A.
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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.

Keywords

sequential recommendationnovelty-seeking traittransfer learning

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Journal of Computer Science and Technology
Volume 35 Issue 2,
March 2020
Pages 305-319
DOI: 10.1007/s11390-020-9945-z
Cite this article:
Zhuang F-Z, Zhou Y-M, Ying H-C, et al. Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining. Journal of Computer Science and Technology, 2020, 35(2): 305-319. https://doi.org/10.1007/s11390-020-9945-z

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Received: 15 July 2019
Revised: 21 January 2020
Published: 27 March 2020
©Institute of Computing Technology, Chinese Academy of Sciences 2020
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