19 citations · 49 across the 15 of their papers we have counts for
16 papers
Rethinking Popularity Bias in Collaborative Filtering via Analytical Vector Decomposition
Lingfeng Liu, Yixin Song, Dazhong Shen +4
Popularity bias fundamentally undermines the personalization capabilities of collaborative filtering (CF) models, causing them to disproportionately recommend popular items while n…
Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models
Yongwen Ren, Chao Wang, Peng Du +3
Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To f…
FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens
Chao Wang, Yixin Song, Jinhui Ye +5
Recently, large language models (LLMs) have been explored for integration with collaborative filtering (CF)-based recommendation systems, which are crucial for personalizing user e…
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…
A Comprehensive Survey on Self-Interpretable Neural Networks
Yang Ji, Ying Sun, Yuting Zhang +7
Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making s…
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…
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
Yunfei Zhang, Chuan Qin, Dazhong Shen +4
During the past few decades, cognitive diagnostics modeling has attracted increasing attention in computational education communities, which is capable of quantifying the learning…
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
Chuan Qin, Le Zhang, Yihang Cheng +8
In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Ind…
Preference or Intent? Double Disentangled Collaborative Filtering
Chao Wang, Hengshu Zhu, Dazhong Shen +2
People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both…
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
Dazhong Shen, Chuan Qin, Chao Wang +3
As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. However, when t…
Drug Package Recommendation via Interaction-aware Graph Induction
Zhi Zheng, Chao Wang, Tong Xu +5
Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation.…
A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring
Dazhong Shen, Qi Zhang, Tong Xu +7
Identifying the arrival times of seismic P-phases plays a significant role in real-time seismic monitoring, which provides critical guidance for emergency response activities. Whil…