69 citations · 113 across the 7 of their papers we have counts for
7 papers
Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation
Ji Dai, Quan Fang, Jun Hu +3
Multimedia recommendation systems leverage user-item interactions and multimodal information to capture user preferences, enabling more accurate and personalized recommendations. D…
Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce
Houye Ji, Ye Tang, Zhaoxin Chen +3
With the rapid development of the short video industry, traditional e-commerce has encountered a new paradigm, video-driven e-commerce, which leverages attractive videos for produc…
Efficient Heterogeneous Graph Learning via Random Projection
Jun Hu, Bryan Hooi, Bingsheng He
Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting…
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Jun Hu, Bryan Hooi, Shengsheng Qian +2
Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. Howeve…
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning
Jun Hu, Shengsheng Qian, Quan Fang +1
Graph Neural Networks (GNNs) have achieved great success in processing graph data by extracting and propagating structure-aware features. Existing GNN research designs various prop…
GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
Desheng Cai, Jun Hu, Quan Zhao +3
In this paper, we present GRecX, an open-source TensorFlow framework for benchmarking GNN-based recommendation models in an efficient and unified way. GRecX consists of core librar…