1. UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing
- Author
-
Zhao, Qifang, Li, Tianyu, Du, Meng, Jiang, Yu, Sun, Qinghui, Wang, Zhongyao, Liu, Hong, and Xu, Huan
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Information Retrieval (cs.IR) ,Computer Science - Information Retrieval ,Machine Learning (cs.LG) - Abstract
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.
- Published
- 2023
- Full Text
- View/download PDF