  Recommender Systems (RS), as an efficient tool to discover users' interested
items from a very large corpus, has attracted more and more attention from
academia and industry. As the initial stage of RS, large-scale matching is
fundamental yet challenging. A typical recipe is to learn user and item
representations with a two-tower architecture and then calculate the similarity
score between both representation vectors, which however still struggles in how
to properly deal with negative samples. In this paper, we find that the common
practice that randomly sampling negative samples from the entire space and
treating them equally is not an optimal choice, since the negative samples from
different sub-spaces at different stages have different importance to a
matching model. To address this issue, we propose a novel method named Unbiased
Model-Agnostic Matching Approach (UMA$^2$). It consists of two basic modules
including 1) General Matching Model (GMM), which is model-agnostic and can be
implemented as any embedding-based two-tower models; and 2) Negative Samples
Debias Network (NSDN), which discriminates negative samples by borrowing the
idea of Inverse Propensity Weighting (IPW) and re-weighs the loss in GMM.
UMA$^2$ seamlessly integrates these two modules in an end-to-end multi-task
learning framework. Extensive experiments on both real-world offline dataset
and online A/B test demonstrate its superiority over state-of-the-art methods.
