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Cross-domain recommendation is an emerging research topic. In the last few years an increasing amount of work has been published in various areas related to the Recommender System field, namely User Modeling, Information Retrieval, Knowledge Management, and Machine Learning. The problem has thus been addressed from distinct perspectives. Hence there are even conflicting definitions of the cross-domain recommendation task, and there is no rigorous comparison of existing approaches. In this paper we provide a formal statement of the problem, and present a review of the state of the art. We also establish a general taxonomy that let us to better characterize, categorize and compare the revised work. Finally, we conclude this review with a survey of interesting research topics on cross-domain recommendation.