Cross-Domain Recommendations with Overlapping Items
Kotkov, D., Wang, S., & Veijalainen, J. (2016). Cross-Domain Recommendations with Overlapping Items. In T. A. Majchrzak, P. Traverso, V. Monfort, & K.-H. Krempels (Eds.), WEBIST 2016 : Proceedings of the 12th International conference on web information systems and technologies. Volume 2 (pp. 131-138). Setúbal: SCITEPRESS. doi:10.5220/0005851301310138
© INSTICC, 2016. This is an author's final draft version of an article whose final and definitive form has been published in the WEBIST Proceedings. Published by SCITEPRESS.
In recent years, there has been an increasing interest in cross-domain recommender systems. However, most existing works focus on the situation when only users or users and items overlap in different domains. In this paper, we investigate whether the source domain can boost the recommendation performance in the target domain when only items overlap. Due to the lack of publicly available datasets, we collect a dataset from two domains related to music, involving both the users’ rating scores and the description of the items. We then conduct experiments using collaborative filtering and content-based filtering approaches for validation purpose. According to our experimental results, the source domain can improve the recommendation performance in the target domain when only items overlap. However, the improvement decreases with the growth of non-overlapping items in different domains.