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Cross-Domain Recommender Systems

  • Iván CantadorEmail author
  • Ignacio Fernández-Tobías
  • Shlomo Berkovsky
  • Paolo Cremonesi

Abstract

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.

Keywords

Association Rule Recommender System User Preference User Profile Target Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Iván Cantador
    • 1
    Email author
  • Ignacio Fernández-Tobías
    • 1
  • Shlomo Berkovsky
    • 2
  • Paolo Cremonesi
    • 3
  1. 1.Universidad Autónoma de MadridMadridSpain
  2. 2.CSIROSydneyAustralia
  3. 3.Politecnico di MilanoMilanItaly

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