---
title: "Cross-Domain Recommendation: Challenges, Progress, and Prospects"
authors:
  - "Feng Zhu"
  - "Yan Wang"
  - "Chaochao Chen"
  - "Jun Zhou"
  - "Longfei Li"
  - "Guanfeng Liu"
doi: "10.24963/ijcai.2021/639"
url: "https://ijcai.org/proceedings/2021/639"
pdf: "https://ijcai.org/proceedings/2021/0639.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4721-4728"
track: "Survey Track"
keywords:
  - "Knowledge representation and reasoning: General"
  - "Multidisciplinary topics and applications: General"
---

# Cross-Domain Recommendation: Challenges, Progress, and Prospects

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

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4721-4728. DOI: [10.24963/ijcai.2021/639](https://doi.org/10.24963/ijcai.2021/639)

## 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.