647 citations · 1.7k across the 12 of their papers we have counts for
12 papers · 1 filter
GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Wei Yinwei, Wang Xiang, Nie Liqiang +2
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interac…
Hierarchical User Intent Graph Network forMultimedia Recommendation
Wei Yinwei, Wang Xiang, He Xiangnan +3
In this work, we aim to learn multi-level user intents from the co-interacted patterns of items, so as to obtain high-quality representations of users and items and further enhance…
Contrastive Learning for Cold-Start Recommendation
Yinwei Wei, Xiang Wang, Qi Li +4
Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use co…
Deconfounded Recommendation for Alleviating Bias Amplification
Wenjie Wang, Fuli Feng, Xiangnan He +2
Recommender systems usually amplify the biases in the data. The model learned from historical interactions with imbalanced item distribution will amplify the imbalance by over-reco…
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang, Tinglin Huang, Dingxian Wang +4
Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs).…
Interactive Path Reasoning on Graph for Conversational Recommendation
Wenqiang Lei, Gangyi Zhang, Xiangnan He +4
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user p…