1 citations · 2 across the 8 of their papers we have counts for
8 papers
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models
Rui Cai, Chao Wang, Qianyi Cai +2
Knowledge Graph-based recommendations have gained significant attention due to their ability to leverage rich semantic relationships. However, constructing and maintaining Knowledg…
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
Shengzhe Zhang, Liyi Chen, Dazhong Shen +2
Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a…
RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes
Xiaoshan Yu, Chuan Qin, Dazhong Shen +4
In the realm of education, both independent learning and group learning are esteemed as the most classic paradigms. The former allows learners to self-direct their studies, while t…
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Leilei Ding, Dazhong Shen, Chao Wang +3
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node informa…
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen +3
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture colla…
Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach
Feihu Jiang, Chuan Qin, Jingshuai Zhang +6
In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured info…