Cross-Domain Recommendation: Challenges, Progress, and Prospects

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

doi:10.24963/ijcai.2021/639

Cross-Domain Recommendation: Challenges, Progress, and Prospects

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

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4721-4728.

https://doi.org/10.24963/ijcai.2021/639

Keywords

Knowledge representation and reasoning: General
Multidisciplinary topics and applications: General

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.

Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-Domain Recommendation: Challenges, Progress, and Prospects. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4721–4728. https://doi.org/10.24963/ijcai.2021/639

@inproceedings{ijcai2021p639,
  title     = {Cross-Domain Recommendation: Challenges, Progress, and Prospects},
  author    = {Zhu, Feng and Wang, Yan and Chen, Chaochao and Zhou, Jun and Li, Longfei and Liu, Guanfeng},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4721--4728},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/639},
  url       = {https://doi.org/10.24963/ijcai.2021/639},
}

TY  - CPAPER
TI  - Cross-Domain Recommendation: Challenges, Progress, and Prospects
AU  - Zhu, Feng
AU  - Wang, Yan
AU  - Chen, Chaochao
AU  - Zhou, Jun
AU  - Li, Longfei
AU  - Liu, Guanfeng
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4721
EP  - 4728
DO  - 10.24963/ijcai.2021/639
UR  - https://doi.org/10.24963/ijcai.2021/639
ER  -