Improving Serendipity and Accuracy in Cross-Domain Recommender Systems
Kotkov, D., Wang, S., & Veijalainen, J. (2017). Improving Serendipity and Accuracy in Cross-Domain Recommender Systems. In V. Monfort, K.-H. Krempels, T. A. Majchrzak, & P. Traverso (Eds.), Web Information Systems and Technologies : 12th International Conference, WEBIST 2016, Rome, Italy, April 23–25, 2016, Revised Selected Papers (pp. 105-119). Lecture Notes in Business Information Processing, 292. Cham: Springer International Publishing AG. doi:10.1007/978-3-319-66468-2_6
Published inLecture Notes in Business Information Processing;292
© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. According to our results, the source domain can improve serendipity in the target domain for both approaches. The source domain decreases accuracy for contentbased filtering and increases accuracy for collaborative filtering. The improvement of accuracy decreases with the growth of non-overlapping items in different domains. ...