Cross-Domain Recommendation: Challenges, Progress, and Prospects

• URL: http://arxiv.org/abs/2103.01696v1
• Date: Tue, 2 Mar 2021 12:58:08 GMT
• Title: Cross-Domain Recommendation: Challenges, Progress, and Prospects
• Authors: Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, Guanfeng Liu
• Abstract summary: 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. In this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and future directions.
• Score: 21.60393384976869
• License: http://creativecommons.org/licenses/by/4.0/
• 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 future directions. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, multi-domain recommendation, 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 research directions in CDR.

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