标题： 长检索器：面向推荐系统的超长序列候选检索 显示英文标题

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

摘要： 精确建模用户超长序列对于工业推荐系统至关重要。 当前方法主要关注在排序阶段利用超长序列，而候选检索阶段的研究仍缺乏探索。 本文提出了LongRetriever，这是一个将超长序列引入推荐系统检索阶段的实用框架。 具体而言，我们提出了上下文训练和多上下文检索，这使得用户序列与候选物品之间能够进行特定于候选的交互，并在基于搜索的范式下确保训练与服务的一致性。 在大规模电子商务平台上进行的大量在线A/B测试表明有统计学意义的提升，证实了该框架的有效性。 目前， LongRetriever已在该平台全面部署，影响数十亿用户。 显示英文摘要

摘要： 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.