UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing
This addresses the cost and complexity problem for merchants using cloud services in private domain marketing, though it is incremental as it builds on existing matching frameworks.
The paper tackles the high cost of using separate models for different marketing tasks by proposing UniMatch, a unified framework that simultaneously handles item recommendation and user targeting with a single model, achieving significant performance gains over state-of-the-art methods while reducing computing and maintenance costs.
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.