Product quantization (PQ) is a popular approach for maximum inner product search (MIPS), which is widely used in ad-hoc retrieval. Recent studies propose differentiable PQ, where the embedding and quantization modules can be trained jointly. However, there is a lack of in-depth understanding of appropriate joint training objectives; and the improvements over non-differentiable baselines are not consistently positive in reality. In this work, we propose Search-oriented Product Quantization (SoPQ), where a novel training objective MCL is formulated. With the minimization of MCL, query and key's matching probability can be maximized for the differentiable PQ. Besides, VCS protocol is designed to facilitate the minimization of MCL, and SQL is leveraged to relax the dependency on labeled data. Extensive experiments on 4 real-world datasets validate the effectiveness of our proposed methods.

本文介绍了匹配导向的产品量化技术(MoPQ)及其训练目标Multinoulli对比损失(MCL)，通过最小化MCL以最大化查询和真实关键字的匹配概率来提高检索准确性。同时，该技术通过可微的跨设备抽样(DCS)来大幅增加对比样本的数量以精确地近似MCL。在四个真实数据集上进行的实验研究表明了MoPQ的有效性。

基于匹配的产品量化技术用于即席检索