UniMatch: A Unified User-Item Matching Framework for the Multi-purpose
  Merchant Marketing
Qifang Zhao Tianyu Li Meng Du Yu Jiang Qinghui Sun Zhongyao Wang Hong Liu Huan Xu
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.