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- W3190386329 abstract "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." @default.
- W3190386329 created "2021-08-16" @default.
- W3190386329 creator A5012589427 @default.
- W3190386329 creator A5036941348 @default.
- W3190386329 creator A5041155283 @default.
- W3190386329 creator A5081759167 @default.
- W3190386329 creator A5082218936 @default.
- W3190386329 date "2022-12-21" @default.
- W3190386329 modified "2023-10-05" @default.
- W3190386329 title "A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions" @default.
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