UMEC: Unified model and embedding compression for efficient recommendation systems

Jiayi Shen, Haotao Wang, Shupeng Gui, Jianchao Tan, Zhangyang Wang, Ji Liu

Keywords: recommendation system, model compression, ADMM, resource constrained

Abstract: The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios. The core part of the system is the Ranking neural network, which is usually a bottleneck of whole system performance during online inference. In this work, we propose a unified model and embedding compression (UMEC) framework to hammer an efficient neural network-based recommendation system. Our framework jointly learns input feature selection and neural network compression together, and solve them as an end-to-end resource-constrained optimization problem using ADMM. Our method outperforms other baselines in terms of neural network Flops, sparse embedding feature size and the number of sparse embedding features. We evaluate our method on the public benchmark of DLRM, trained over the Kaggle Criteo dataset. The codes can be found at https://github.com/VITA-Group/UMEC.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

One-sentence Summary: We propose a unified model and embedding compression (UMEC) framework to hammer an efficient neural network-based recommendation system.