Paper Info
Title
Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining.
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.
Year
DOI
Venue
2020
10.1007/s11390-020-9945-z
Journal of Computer Science and Technology
Keywords
DocType
Volume
sequential recommendation, novelty-seeking trait, transfer learning
Journal
35
Issue
ISSN
Citations 
2
1000-9000
0
PageRank 
References 
Authors
0.34
24
8
Name
Order
Citations
PageRank
Fuzhen Zhuang182775.28
Zhou Yingmin211.37
Haochao Ying37310.03
Fuzheng Zhang498441.96
Xiang Ao5348.49
Xing Xie69105527.49
Qing He775480.58
Hui Xiong84958290.62