{"type":"ScholarlyArticle","title":"Cross-Domain Recommendation: Challenges, Progress, and Prospects","authors":[{"name":"Feng Zhu","slug":"feng-zhu","given":"Feng","family":"Zhu","affiliation":""},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":""},{"name":"Chaochao Chen","slug":"chaochao-chen","given":"Chaochao","family":"Chen","affiliation":""},{"name":"Jun Zhou","slug":"jun-zhou","given":"Jun","family":"Zhou","affiliation":""},{"name":"Longfei Li","slug":"longfei-li","given":"Longfei","family":"Li","affiliation":""},{"name":"Guanfeng Liu","slug":"guanfeng-liu","given":"Guanfeng","family":"Liu","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/639","url":"https://ijcai.org/proceedings/2021/639","pdf":"https://ijcai.org/proceedings/2021/0639.pdf","markdown":"https://ijcai.org/proceedings/2021/639.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/639","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Survey Track","track":"Survey Track","number":639,"pages":{"first":4721,"last":4728},"keywords":["Knowledge representation and reasoning: General","Multidisciplinary topics and applications: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}