将超长用户行为序列引入推荐召回阶段，提升精准度。
LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
- 通过上下文训练与多上下文召回，实现用户序列与候选项的动态交互
- 在线测试显示效果显著提升，已服务超百亿用户
- 适合需要处理超长序列的工业级推荐系统
精确建模用户超长序列对工业级推荐系统至关重要。当前方法多聚焦于排序阶段利用超长序列，而召回阶段的研究仍不充分。本文提出 LongRetriever，一个将超长序列引入推荐召回阶段的实用框架。具体而言，我们设计了上下文内训练和多上下文召回机制，实现用户序列与候选项之间的候选特定交互，并在基于搜索的范式下保障训练与服务的一致性。在大规模电商平台上开展的大量在线 A/B 测试表明，该框架带来统计显著的效果提升。目前，LongRetriever 已在平台全面部署，影响超百亿用户。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。