Abstract
Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multi-domain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that cross-domain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it.
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Sahebi, S., Brusilovsky, P. (2013). Cross-Domain Collaborative Recommendation in a Cold-Start Context: The Impact of User Profile Size on the Quality of Recommendation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_25
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DOI: https://doi.org/10.1007/978-3-642-38844-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38843-9
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