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 be- come 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 recommenda- tions 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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