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.

翻译：在进行云服务的私域营销时，商户通常需为多种营销目标购买不同的机器学习模型，导致成本高昂。我们提出一种统一的用户-物品匹配框架，仅用单一模型即可同时进行物品推荐与用户定向。我们通过多项式分布对用户-物品交互矩阵建模，从实证角度证明了上述并发建模的可行性，并为此提出了一种双向偏差校正的NCE损失函数。该损失函数通过修正批次内负采样导致的用户与物品偏差，引导模型学习用户-物品联合概率$p(u,i)$而非条件概率$p(i|u)$或$p(u|i)$。此外，我们的框架具有模型无关性，可灵活适配不同模型架构。大量实验表明，与现有最先进方法相比，该框架在显著降低计算资源与日常维护成本的同时，实现了性能的大幅提升。