Accuracy and diversity in cross-domain recommendations for cold-start users with positive-only feedback

Computing useful recommendations for cold-start users is a major challenge in the design of recommender systems, and additional data is often required to compensate the scarcity of user feedback. In this paper we address such problem in a target domain by exploiting user preferences from a related auxiliary domain. Following a rigorous methodol- ogy for cold-start, we evaluate a number of recommenda- tion methods on a dataset with positive-only feedback in the movie and music domains, both in single and cross-domain scenarios. Comparing the methods in terms of item rank- ing accuracy, diversity and catalog coverage, we show that cross-domain preference data is useful to provide more accu- rate suggestions when user feedback in the target domain is scarce or not available at all, and may lead to more diverse recommendations depending on the target domain. More- over, evaluating the impact of the user profile size and di- versity in the source domain, we show that, in general, the quality of target recommendations increases with the size of the prole, but may deteriorate with too diverse profiles.