A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
It provides a structured overview for researchers and practitioners in recommendation systems, but it is incremental as it synthesizes existing work without introducing new methods.
This survey addresses the lack of systematic reviews on cross-domain recommendation (CDR) by proposing a two-level taxonomy to classify recommendation scenarios and tasks, summarizing existing methods, and organizing datasets.
Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.