Design and Evaluation of Cross-Domain Recommender Systems

• Published in: Recommender Systems Handbook pp. 485 - 516
• Main Authors: Dacrema, Maurizio Ferrari, Cantador, Iván, Fernández-Tobías, Ignacio, Berkovsky, Shlomo, Cremonesi, Paolo
• Format: Book Chapter
• Language: English
• Published: New York, NY Springer US 2022
• ISBN: 9781071621967; 1071621963
• DOI: 10.1007/978-1-0716-2197-4_13

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.