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Design and Evaluation of Cross-Domain Recommender Systems

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Recommender Systems Handbook

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 spectrum 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, or enabling cross-selling recommendations for items from multiple domains. Cross-domain recommender systems, thus, aim to 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, and analytically categorize and describe various recommendation techniques, from the simple legacy ones to the sophisticated ones based on deep-learning.

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Notes

  1. 1.

    The reader is referred to Chap. 29 for an extensive discussion on the different methods used to evaluate recommender systems.

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Authors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Maurizio Ferrari Dacrema & Paolo Cremonesi

  2. Universidad Autónoma de Madrid, Madrid, Spain

    Iván Cantador & Ignacio Fernández-Tobías

  3. Macquarie University, Sydney, Australia

    Shlomo Berkovsky

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  1. Maurizio Ferrari Dacrema
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  2. Iván Cantador
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  3. Ignacio Fernández-Tobías
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  5. Paolo Cremonesi
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Correspondence to Paolo Cremonesi .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Dacrema, M.F., Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P. (2022). Design and Evaluation of Cross-Domain Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_13

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