UMEC: Unified model and embedding compression for efficient recommendation systems

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. Hammering an efficient neural network-based recommendation system involves entangled challenges of compressing both the network parameters and the feature embedding inputs. We propose a unified model and embedding compression (UMEC) framework to jointly learn input feature selection and neural network compression together, which is formulated as a resource-constrained optimization problem and solved using the alternating direction method of multipliers (ADMM) algorithm. Experimental results on public benchmarks show that our UMEC framework notably outperforms other non-integrated baseline methods. The codes can be found at https://github.com/VITA-Group/UMEC .