中文标题#
長檢索器：面向推薦系統的超長序列候選檢索
英文标题#
LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
中文摘要#
精確建模用戶超長序列對於工業推薦系統至關重要。 當前方法主要關注在排序階段利用超長序列，而候選檢索階段的研究仍缺乏探索。 本文提出了 LongRetriever，這是一個將超長序列引入推薦系統檢索階段的實用框架。 具體而言，我們提出了上下文訓練和多上下文檢索，這使得用戶序列與候選物品之間能夠進行特定於候選的互動，並在基於搜索的範式下確保訓練與服務的一致性。 在大規模電子商務平台上進行的大量在線 A/B 測試表明有統計學意義的提升，證實了該框架的有效性。 目前， LongRetriever 已在該平台全面部署，影響數十億用戶。
英文摘要#
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.
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