  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.
