Sequential Transfer Learning

Abstract

Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. This paper studies the problem of mining novelty seeking trait across domains to improve the recommendation performance in target domain. We propose an efficient model, CDNST, which significantly improves the recommendation performance by transferring the knowledge from auxiliary source domain. We conduct extensive experiments on three domain datasets crawled from Douban (www.douban.com) to demonstrate the effectiveness of the proposed model. Moreover, we find that the property of sequential data affects the performance of CDNST.