Cross-Domain Recommendation: Challenges, Progress, and Prospects

By Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, Guanfeng Liu. International Joint Conference on Artificial Intelligence, 2021

301 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3a665d50eceb7008086cfa1180cdf6dd5d061ba3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Yupeng Hou
2. Ji-rong Wen
3. Bolin Ding
4. Hongyu Lu
5. Wayne Xin Zhao
6. Ruobing Xie
7. Julian McAuley

In Information Systems

1. Wayne Xin Zhao
2. Yupeng Hou
3. Ji-rong Wen
4. Hongyu Lu
5. Jiacheng Li
6. Bolin Ding
7. Ruobing Xie
8. Junjie Zhang

In Management Science and Operations Research

1. Jiacheng Li
2. Ming Wang
3. Jin Li
4. Jingbo Shang
5. Xin Shen
6. Jinmiao Fu
7. Wayne Xin Zhao
8. Ji-rong Wen

Citations in the corpus

• Text Is All You Need: Learning Language Representations for Sequential Recommendation (2023, background): “Data sparsity and cold-start item understanding issues are challenging in recommender systems and recent studies [33, 39, 40] explore transferring knowledge across different domains to improve the recommendation at the target domain.”
• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, background): “We would like to take a more focused study on how they improve the recommendation performance of LLMs. Instruction tuning improves the ranking abilities of LLMs. Existing works show that instruction tuning significantly improves the generalization abilities of LLMs on unseen tasks [27, 32].”
• Towards Universal Sequence Representation Learning for Recommender Systems (2022, background): “To deal with the data sparsity and cold-start issues in the recommender systems, various works aim to leverage behavior information from other domains [39] or platforms [14] to improve recommendation performance of the target domain or both domains [38].”
• Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System (2023, background): “Lastly, current recommender systems face challenges in making recommendations across multiple domains [26].”