Cross-domain recommendation: challenges, progress, and prospects

Feng Zhu, Yan Wang, Chaochao Chen*, Jun Zhou, Longfei Li, Guanfeng Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and prospects. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, single-target multi-domain recommendation (MDR), dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising prospects in CDR.
Original languageEnglish
Title of host publicationProceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021)
EditorsZhi-Hua Zhou
Place of PublicationFreiburg, Germany
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4721-4728
Number of pages8
ISBN (Electronic)9780999241196
DOIs
Publication statusPublished - 2021
Event30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Montreal, Canada
Duration: 19 Aug 202127 Aug 2021

Conference

Conference30th International Joint Conference on Artificial Intelligence, IJCAI 2021
CountryCanada
CityMontreal
Period19/08/2127/08/21

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