A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects
Overview
- Authors: Hao Zhang, Mingyue Cheng, Qi Liu, Junzhe Jiang, Xianquan Wang, Rujiao Zhang, et al. (8 authors total)
- Published: 2025-03-18
- Source: https://arxiv.org/abs/2503.14110
- Type: Survey
- Category: Recommender Engine Surveys
- A unified perspective that organizes scattered CDR work into a comparable framework.
- A clear decomposition of method components (representation learning, retrievers, rerankers, planners, generators, feedback mechanisms).
- Reproducible benchmarks, datasets, and taxonomy tables that lower the entry barrier for new researchers.
- Discussion of interfaces with emerging paradigms such as LLM tool use, reinforcement learning, and multi-agent collaboration.
- Explicit open problems: evaluation trustworthiness, latency and cost, hallucination and safety, cross-lingual and multimodal extension.
- Dense retrieval: high recall, low latency; suitable for first-stage retrieval.
- Late interaction (e.g., ColBERT): higher accuracy but larger indexes.
- Generative IR: directly "generates" documents via tokens or docids, simplifying cascades.
- Agentic search: models search as sequential decision-making, supporting multi-hop reasoning and self-reflection.
- 2019–2021: BERT reranking and DPR established the foundation for neural retrieval.
- 2022–2023: RAG and FreshLLM drove retrieval-generation fusion.
- 2024 onward: conversational/agentic search and Gen-RecSys expanded rapidly.
- 2025–2026: RL-trained search agents, Deep Research, and GraphRAG became new growth areas.
- Datasets: MS MARCO, BEIR, Natural Questions, domain-specific corpora, public recommendation sets.
- Metrics: nDCG@10, MRR, Recall@k, Hit@k, human preference, task success rate, latency and token cost.
- Baselines: BM25, dense retrieval, cross-encoder reranking, retrieval-free LLMs, commercial search APIs.
- Original paper: A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects. https://arxiv.org/abs/2503.14110
One-line Summary
A systematic survey of cross-domain recommendation (CDR), organizing recent progress around the CDR pipeline and outlining open challenges and future directions.
Background and Motivation
Recommender systems (RS) are crucial tools for information filtering in real-world scenarios. Cross-domain recommendation (CDR) has been widely explored in recent years to improve target-domain recommendations with the help of other domains. While CDR technology has developed rapidly, a comprehensive survey summarizing recent works has been lacking. This paper fills that gap by summarizing progress and prospects based on the main CDR procedure.
Core Contributions
Taxonomy
The survey organizes CDR progress around the main pipeline stages:
1. Cross Domain Relevance 2. Cross Domain Interaction 3. Cross Domain Representation Enhancement 4. Model Optimization
Methodological threads commonly contrasted in the survey space include:
Timeline of Research Evolution
Evaluation Paradigms
Quantitative results should be verified against the original PDF tables.
Key Insights
1. Architecture: cascaded retrieval + rerank + generation remains mainstream, but agentic paradigms make retrieval strategy itself learnable. 2. Data: high-quality instruction data and click/session logs are both critical; synthetic data risks knowledge leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge needs cross-validation with human evaluation. 4. Product: latency, cost, explainability, and safety are hard constraints for industrial deployment.
Open Problems and Future Directions
The authors highlight: lack of unified benchmarks, non-reproducible private data, LLM evaluation bias, and safety/cost constraints of agentic systems. Future work includes finer-grained process supervision, joint retrieval-reasoning training, enterprise metadata governance, and multimodal/cross-lingual consistency.
Original Abstract (verbatim)
> Recommender systems (RS) have become crucial tools for information filtering in various real world scenarios. And cross domain recommendation (CDR) has been widely explored in recent years in order to provide better recommendation results in the target domain with the help of other domains. The CDR technology has developed rapidly, yet there is a lack of a comprehensive survey summarizing recent works. Therefore, in this paper, we will summarize the progress and prospects based on the main procedure of CDR, including Cross Domain Relevance, Cross Domain Interaction, Cross Domain Representation Enhancement and Model Optimization. To help researchers better understand and engage in this field, we also organize the applications and resources, and highlight several current important challenges and future directions of CDR. More details of the survey articles are available at https://github.com/USTCAGI/Awesome-Cross-Domain Recommendation-Papers-and-Resources.