Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation

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 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.