Chang‐Dong Wang

BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism

2021 · arXiv (Cornell University)

Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning tries to learn a …

G$^3$SR: Global Graph Guided Session-based Recommendation

2022 · arXiv (Cornell University)

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each …

Vision Transformer for Contrastive Clustering

2022 · arXiv (Cornell University)

Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation learning. Besides ViT, contrastive learning is another popular research topic recently. …

Heterogeneous Tri-stream Clustering Network

2023 · arXiv (Cornell University)

Contrastive deep clustering has recently gained significant attention with its ability of joint contrastive learning and clustering via deep neural networks. Despite the rapid progress, previous works mostly require both positive and negative sample pairs …

Simple One-Step Multi-View Clustering With Fast Similarity and Cluster Structure Learning

2025 · IEEE Signal Processing Letters

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these …

A Structure-Aware Fair Recommendation Approach Based on Counterfactual Dynamic Hypergraphs

2025 · ACM Transactions on Intelligent Systems and Technology

Unfair recommendations stem from user-sensitive attributes and information transmission biases. Graph-structured data can provide more balanced information for fair recommendations by capturing multidimensional user–item interactions. However, graph-based fair recommendation still faces some challenges: Traditional graphs …

Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

2018 · IEEE Transactions on Cybernetics

Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, …