Cross-domain recommender systems

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

Research output: Chapter in Book/Report/Conference proceedingChapter

72 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationRecommender systems handbook
EditorsFrancesco Ricci, Lior Rokach, Bracha Shapira
PublisherSpringer, Springer Nature
Chapter27
Pages919-959
Number of pages41
EditionSecond
ISBN (Electronic)9781489976376
ISBN (Print)9781489976369
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

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

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  • Cite this

    Cantador, I., Fernández-Tobias, I., Berkovsky, S., & Cremonesi, P. (2015). Cross-domain recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (Second ed., pp. 919-959). Springer, Springer Nature. https://doi.org/10.1007/978-1-4899-7637-6_27