LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866911118485618688 |
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| author | Ren, Qin Chai, Zheng Xiao, Xijun Zheng, Yuchao Wu, Di |
| author_facet | Ren, Qin Chai, Zheng Xiao, Xijun Zheng, Yuchao Wu, Di |
| contents | Precisely modeling user ultra-long sequences is critical for industrial recommender systems. Current approaches predominantly focus on leveraging ultra-long sequences in the ranking stage, whereas research for the candidate retrieval stage remains under-explored. This paper presents LongRetriever, a practical framework for incorporating ultra-long sequences into the retrieval stage of recommenders. Specifically, we propose in-context training and multi-context retrieval, which enable candidate-specific interaction between user sequence and candidate item, and ensure training-serving consistency under the search-based paradigm. Extensive online A/B testing conducted on a large-scale e-commerce platform demonstrates statistically significant improvements, confirming the framework's effectiveness. Currently, LongRetriever has been fully deployed in the platform, impacting billions of users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15486 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation Ren, Qin Chai, Zheng Xiao, Xijun Zheng, Yuchao Wu, Di Information Retrieval Precisely modeling user ultra-long sequences is critical for industrial recommender systems. Current approaches predominantly focus on leveraging ultra-long sequences in the ranking stage, whereas research for the candidate retrieval stage remains under-explored. This paper presents LongRetriever, a practical framework for incorporating ultra-long sequences into the retrieval stage of recommenders. Specifically, we propose in-context training and multi-context retrieval, which enable candidate-specific interaction between user sequence and candidate item, and ensure training-serving consistency under the search-based paradigm. Extensive online A/B testing conducted on a large-scale e-commerce platform demonstrates statistically significant improvements, confirming the framework's effectiveness. Currently, LongRetriever has been fully deployed in the platform, impacting billions of users. |
| title | LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.15486 |