In the last few years, cross-domain recommender systems emerged in order to improve and alleviate problems of single-domain recommender systems. Despite the great number of cross-domain recommender system approaches, there is a lack of studies concerned about the use of contextual features in cross domain recommender systems. The context-aware approach uses different contextual information (e.g., Location, time, and mood) in order to improve recommendations, where context can be treated as a bridge between different domains. In this paper, we investigate the adoption of two context-aware approaches in a cross-domain recommender system in order to improve its recommendation accuracy. For that, we describe the context aware cross-domain recommendation problem and the proposed context-aware algorithms. An experimental evaluation performed using a real dataset indicates that context-aware techniques can be a good approach in order to improve the cross-domain recommendation accuracy.
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