Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining
作者机构：Key Laboratory of Intelligent Information Processing of Chinese Academy of SciencesInstitute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of SciencesBeijing 100049China School of Public HealthZhejiang University School of MedicineHangzhou 310027China Meituan-Dianping GroupBeijing 100102China Microsoft Research AsiaBeijing 100080China Department of Management Science and Information SystemsRutgers UniversityNew Jersey 07102U.S.A.
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2020年第35卷第2期
页 面：305-319页
核心收录：
学科分类：0808[工学-电气工程] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：The work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004300 the National Natural Science Foundation of China under Grant Nos. U1836206, U1811461, 61773361 the Project of Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No. 2017146
主 题：sequential recommendation novelty-seeking trait transfer learning
摘 要：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.
维普期刊数据库