Cross-domain recommender systems

Iván Cantador, Ignacio Fernández-Tobias, Shlomo Berkovsky, Paolo Cremonesi

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

151 Citations (Scopus)


The proliferation of e-commerce sites and online social media has allowed users to provide preference feedback and maintain profiles in multiple systems, reflecting a variety of their tastes and interests. Leveraging all the user preferences available in several systems or domains may be beneficial for generating more encompassing user models and better recommendations, e.g., through mitigating the cold-start and sparsity problems in a target domain, or enabling personalized cross-selling recommendations for items from multiple domains. Cross-domain recommender systems, thus, aim to generate or enhance recommendations in a target domain by exploiting knowledge from source domains. In this chapter, we formalize the cross-domain recommendation problem, unify the perspectives from which it has been addressed, analytically categorize, describe and compare prior work, and identify open issues for future research.
Original languageEnglish
Title of host publicationRecommender systems handbook
EditorsFrancesco Ricci, Lior Rokach, Bracha Shapira
PublisherSpringer, Springer Nature
Number of pages41
ISBN (Electronic)9781489976376
ISBN (Print)9781489976369
Publication statusPublished - 2015
Externally publishedYes


  • association rule
  • recommender system
  • user preference
  • user profile
  • taget domain


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