发表机构
Meta Platforms, Inc.（元平台公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对推荐系统检索阶段排序目标与索引结构不一致的问题，提出OneShot框架，将索引学习与排序目标联合，突破点积瓶颈，部署于Instagram短视频推荐系统，提升了召回率与效率。
AI 中文摘要
在现代推荐系统中，检索是核心阶段，负责将数十亿候选物品筛选至数千个，再交由后续的精排模块处理。为了让这种大规模搜索既有效又高效，系统依赖排序的准确性和索引的效率。然而，这两个目标传统上是不一致的：前者优化排序预测与用户行为的对齐，后者优化物品表示的结构分组，以便在数十亿候选中快速搜索。因此，尽管人们为扩大检索的交互建模付出了大量努力，但这些方法仍受到排序目标与基于邻近学习的索引之间结构不一致的根本限制。本研究提出一种全新的整体检索框架OneShot，以解决这一长期存在的二分问题。它是一种端到端的模型内索引学习框架，能使索引学习与排序目标自然对齐。以这种联合学习为结构基础，OneShot通过神经打分扩展交互建模，突破了持续存在的点积瓶颈，提升了检索的表达能力。OneShot已全面部署于Instagram的工业级短视频推荐系统，显著提升了用户每日会话量、参与度和停留时长。此外，在运营排序量下，OneShot实现了20%的召回率提升；在同等召回水平下，效率提升达10倍。
英文摘要
In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this massive search effective and efficient, the system relies on ranking accuracy and indexing efficiency. However, these two objectives are traditionally misaligned: while the former optimizes for the alignment between ranking predictions and user behavior, the latter optimizes for a structural grouping of item representations which enables fast search among billions of candidates. Thus, despite extensive efforts to scale up interaction modeling for retrieval, they remain fundamentally limited by the structural misalignment between the ranking objectives and the proximity-learned index. In this work, we address this long-standing dichotomy by proposing a new holistic retrieval framework, OneShot. It is an end-to-end, in-model index learning framework that natively aligns index learning with ranking objectives. Using this joint learning as a structural foundation, OneShot pushes the boundaries of retrieval expressiveness by scaling interaction modeling with neural scoring beyond the persistent dot-product bottleneck. OneShot is fully deployed in Instagram's industrial short-video recommendation system, driving significant wins in user daily sessions, engagement, and time-spent. Additionally, OneShot achieves a $20\%$ recall gain at the operational ranking volume and a 10x efficiency improvement at an equivalent recall level.
