LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation

• URL: http://arxiv.org/abs/2508.15486v2
• Date: Mon, 25 Aug 2025 06:37:46 GMT
• Title: LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
• Authors: Qin Ren, Zheng Chai, Xijun Xiao, Yuchao Zheng, Di Wu,
• Abstract summary: LongRetriever is a framework for incorporating ultra-long sequences into the retrieval stage of recommenders.<n>LongRetriever has been fully deployed in a large-scale e-commerce platform, impacting billions of users.
• Score: 9.134763430655488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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