227 citations · 557 across the 47 of their papers we have counts for
9 papers · 1 filter
Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
Yinan Zhang, Boyang Li, Yong Liu +2
Proper initialization is crucial to the optimization and the generalization of neural networks. However, most existing neural recommendation systems initialize the user and item em…
A Hybrid Bandit Framework for Diversified Recommendation
Qinxu Ding, Yong Liu, Chunyan Miao +2
The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely…
Pre-training Graph Transformer with Multimodal Side Information for Recommendation
Yong Liu, Susen Yang, Chenyi Lei +5
Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training…
Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
Susen Yang, Yong Liu, Yonghui Xu +3
Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation. Existing GNN-based methods explicitly model the dependency between an ent…
Learning Hierarchical Review Graph Representations for Recommendation
Yong Liu, Susen Yang, Yinan Zhang +3
The user review data have been demonstrated to be effective in solving different recommendation problems. Previous review-based recommendation methods usually employ sophisticated…
Bandit Learning for Diversified Interactive Recommendation
Yong Liu, Yingtai Xiao, Qiong Wu +2
Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions. Previous methods mainly focus o…