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.
|Titolo:||Cross-domain recommender systems|
|Autori interni:||CREMONESI, PAOLO|
|Data di pubblicazione:||2015|
|Appare nelle tipologie:||02.1 Contributo in Volume|