Abstract
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 content-based filtering and increases accuracy for collaborative filtering. The improvement of accuracy decreases with the growth of non-overlapping items in different domains.
Shuaiqiang Wang—The research was conducted while the author was working for the University of Jyvaskyla, Finland.
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Acknowledgement
The research at the University of Jyväskylä was performed in the MineSocMed project, partially supported by the Academy of Finland, grant #268078. The communication of this research was supported by Daria Wadsworth.
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Kotkov, D., Wang, S., Veijalainen, J. (2017). Improving Serendipity and Accuracy in Cross-Domain Recommender Systems. In: Monfort, V., Krempels, KH., Majchrzak, T., Traverso, P. (eds) Web Information Systems and Technologies. WEBIST 2016. Lecture Notes in Business Information Processing, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-319-66468-2_6
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