Cross-domain Novelty Seeking Trait Mining for Sequential Recommendation Fuzhen Zhuang Yingmin Zhou Fuzheng Zhang IIP, ICT, CAS, Beijing, China IIP, ICT, CAS, Beijing, China Microsoft Research Asia
[email protected] [email protected] [email protected] Xiang Ao Xing Xie Qing He IIP, ICT, CAS, Beijing, China Microsoft Research Asia IIP, ICT, CAS, Beijing, China
[email protected] [email protected] [email protected] ABSTRACT 1 INTRODUCTION Transfer learning has attracted a large amount of interest and re- Personalized recommendation plays a very important role in the search in last decades, and some efforts have been made to build rapid development of E-commerce. To make more precise recom- more precise recommendation systems. Most previous transfer rec- mendations for personal needs, we should understand users’ prefer- ommendation systems assume that the target domain shares the ence propensity or profiles according to their historical behaviors. same/similar rating patterns with the auxiliary source domain, which For example, on the well known E-commerce website Amazon, we is used to improve the recommendation performance. However, to may make recommendations to one user if he shares the similar the best of our knowledge, almost these works do not consider the consuming behaviors with other ones, or according to his histori- characteristics of sequential data. In this paper, we study the new cal consuming behaviors. Therefore, recommendation system has cross-domain recommendation scenario for mining novelty-seeking attracted vast amount of interest and research in recent years to han- trait. Recent studies in psychology suggest that novelty-seeking dle the information overload problem and make predictions [3, 20].