SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems

no code implementations • 18 Aug 2023 • Beichuan Zhang, Chenggen Sun, Jianchao Tan, Xinjun Cai, Jun Zhao, Mengqi Miao, Kang Yin, Chengru Song, Na Mou, Yang song

Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in industrial recommender systems, and hence the increase of computing and storage costs.

Model Compression Quantization +1

HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning

no code implementations • 7 Sep 2021 • Xinjun Cai, Jiaxing Shang, Fei Hao, Dajiang Liu, Linjiang Zheng

Based on these observations, we propose a new heterogeneous graph neural network model named HMSG to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors.

Attribute Graph Neural Network +3