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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.10835 |
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| _version_ | 1866914971761246208 |
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| author | Huang, Heyuan Lou, Xingyu Chen, Chaochao Cheng, Pengxiang Xin, Yue He, Chengwei Liu, Xiang Wang, Jun |
| author_facet | Huang, Heyuan Lou, Xingyu Chen, Chaochao Cheng, Pengxiang Xin, Yue He, Chengwei Liu, Xiang Wang, Jun |
| contents | Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendation systems (RS). Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. Specifically, We first simulate the industrial RS environment that maintains respective models in multiple domains, each of them is trained in the incremental mode. Then, for improving the effectiveness, we design two extractors to fully extract domain-invariant information from the latest source domain models at the domain level and the representation level respectively. Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference. Experiments conducted on one production dataset and two public datasets verify the effectiveness and efficiency of DIIT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10835 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation Huang, Heyuan Lou, Xingyu Chen, Chaochao Cheng, Pengxiang Xin, Yue He, Chengwei Liu, Xiang Wang, Jun Information Retrieval Machine Learning Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendation systems (RS). Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. Specifically, We first simulate the industrial RS environment that maintains respective models in multiple domains, each of them is trained in the incremental mode. Then, for improving the effectiveness, we design two extractors to fully extract domain-invariant information from the latest source domain models at the domain level and the representation level respectively. Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference. Experiments conducted on one production dataset and two public datasets verify the effectiveness and efficiency of DIIT. |
| title | DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2410.10835 |