Engineering

Aerospace engineering

Pure mathematics

Biology

Taxonomy (biology)

Cold start (automotive)

Field (mathematics)

Computer science

Botany

Mathematics

Leverage (statistics)

Domain (mathematical analysis)

Mathematical analysis

Data science

Artificial intelligence

Information retrieval

Recommender system

ACM transactions on office information systems

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. Since the early 2010s, 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 article, we first proposed a two-level taxonomy of cross-domain recommendation that 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.

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions