  Transfer learning has attracted a large amount of interest and research in
last decades, and some efforts have 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, to the best of our knowledge, almost these works do not
consider the characteristics of sequential data. In this paper, we study the
new cross-domain recommendation scenario for 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 performing 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 proposed 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 data sets crawled from Douban (www.douban.com) to
demonstrate the effectiveness of the proposed model. Moreover, we analyze how
the temporal property of sequential data affects the performance of CDNST, and
conduct simulation experiments to validate our analysis.
