A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916655475458048 |
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| author | Zhang, Hao Cheng, Mingyue Liu, Qi Jiang, Junzhe Wang, Xianquan Zhang, Rujiao Lei, Chenyi Chen, Enhong |
| author_facet | Zhang, Hao Cheng, Mingyue Liu, Qi Jiang, Junzhe Wang, Xianquan Zhang, Rujiao Lei, Chenyi Chen, Enhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14110 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects Zhang, Hao Cheng, Mingyue Liu, Qi Jiang, Junzhe Wang, Xianquan Zhang, Rujiao Lei, Chenyi Chen, Enhong Information Retrieval 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. |
| title | A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2503.14110 |