系统梳理跨域推荐关键技术与未来方向。
A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects
- 按四大环节分类跨域推荐方法：相关性、交互、表示增强与优化。
- 总结主流技术进展，归纳典型应用场景与资源库。
- 适合推荐系统研究者入门与追踪前沿挑战。
推荐系统在现实场景中对信息过滤至关重要。近年来，跨域推荐（CDR）被广泛研究，旨在借助其他领域数据提升目标域的推荐效果。尽管CDR技术发展迅速，但缺乏全面综述。本文从CDR主要流程出发，系统梳理了跨域相关性、跨域交互、跨域表示增强及模型优化四个关键环节的研究进展，并整理了应用案例与可用资源，指出当前重要挑战与未来方向。更多详情见https://github.com/USTCAGI/Awesome-Cross-Domain Recommendation-Papers-and-Resources。
原文摘要 · Abstract (English)
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.
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