Top-N recommendations on Unpopular Items with Contextual Knowledge

Traditional recommender systems provide recommendations of items to users; recently, some of them also consider the context related to predictions. In this paper we propose a technique that relies on classical recommendation algorithms and post-filters recommendations on the basis of contextual information available for them. Association rules are exploited to identify the most significant correlations among context and item characteristics. The mined rules are used to filter the predictions performed by traditional recommender systems to provide contextualized recommendations. Our experimental results show that the proposed approach allows improving the output of classical algorithms proposed in the literature, especially in the case of unpopular items.