Cross-domain recommender systems: A survey of the State of the Art Ignacio Fernández-Tobías 1 , Iván Cantador 1 , Marius Kaminskas 2 , Francesco Ricci 2 1 Escuela Politécnica Superior Universidad Autónoma de Madrid 28049 Madrid, Spain {i.fernandez, ivan.cantador}@uam.es 2 Faculty of Computer Science Free University of Bozen-Bolzano 39100 Bolzano, Italy {mkaminskas, fricci}@unibz.it Abstract. 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. Keywords: recommender systems, cross-domain recommender systems, knowledge integration, transfer learning 1 Introduction The huge and ever increasing amount, complexity and heterogeneity of available digital information overwhelm the human processing capabilities in a wide array of information seeking and e-commerce tasks. To cope with information overload recommender systems have been introduced to filter those items –Web pages, images, videos, audio– that are of low relevance or utility for the user, and present only a small selection better suiting the user’s tastes, interests, and priorities. Often these suggestions are presented while the user is browsing an information service, and without requiring her to launch explicit search queries, as is usually done in information retrieval systems. Recommender systems are an active research field and are being used successfully in numerous e-commerce and leisure Web sites such as Amazon, Netflix, YouTube,