  When doing private domain marketing with cloud services, the merchants
usually have to purchase different machine learning models for the multiple
marketing purposes, leading to a very high cost. We present a unified user-item
matching framework to simultaneously conduct item recommendation and user
targeting with just one model. We empirically demonstrate that the above
concurrent modeling is viable via modeling the user-item interaction matrix
with the multinomial distribution, and propose a bidirectional bias-corrected
NCE loss for the implementation. The proposed loss function guides the model to
learn the user-item joint probability $p(u,i)$ instead of the conditional
probability $p(i|u)$ or $p(u|i)$ through correcting both the users and items'
biases caused by the in-batch negative sampling. In addition, our framework is
model-agnostic enabling a flexible adaptation of different model architectures.
Extensive experiments demonstrate that our framework results in significant
performance gains in comparison with the state-of-the-art methods, with greatly
reduced cost on computing resources and daily maintenance.
