A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects

2025-03-18

AI Summary

Cross-domain recommendation (CDR) lacks a systematic, up-to-date survey, hindering theoretical advancement and practical adoption. Method: We propose the first four-stage evolutionary taxonomy—cross-domain correlation modeling → interactive co-learning → representation enhancement → model optimization—synthesizing 130+ mainstream works from 2012 to 2024. Through bibliometric analysis, technical attribution, and systematic comparative evaluation, we identify persistent challenges—including cold-start, heterogeneity, and privacy preservation—and distill transferable technical paradigms. Contribution/Results: We release an open, extensible CDR knowledge base featuring curated open-source implementations, benchmark datasets, and real-world application cases. This work establishes a unified theoretical framework for algorithm design and provides actionable guidelines for industrial deployment, significantly enhancing the systematicity, reproducibility, and translational impact of CDR research.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

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.

Problem

• Summarizes progress and prospects of cross-domain recommendation (CDR).
• Identifies challenges and future directions in CDR research.
• Organizes applications and resources for better understanding of CDR.

Innovation

• Summarizes cross-domain recommendation progress
• Focuses on relevance, interaction, representation enhancement
• Highlights challenges and future research directions