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When doing private domain marketing with cloud services, the merchantsusually have to purchase different machine learning models for the multiplemarketing purposes, leading to a very high cost. We present a unified user-itemmatching framework to simultaneously conduct item recommendation and usertargeting with just one model. We empirically demonstrate that the aboveconcurrent modeling is viable via modeling the user-item interaction matrixwith the multinomial distribution, and propose a bidirectional bias-correctedNCE loss for the implementation. The proposed loss function guides the model tolearn the user-item joint probability $p(u,i)$ instead of the conditionalprobability $p(i|u)$ or $p(u|i)$ through correcting both the users and items'biases caused by the in-batch negative sampling. In addition, our framework ismodel-agnostic enabling a flexible adaptation of different model architectures.Extensive experiments demonstrate that our framework results in significantperformance gains in comparison with the state-of-the-art methods, with greatlyreduced cost on computing resources and daily maintenance.

UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing