1 citations · 1 across the 9 of their papers we have counts for
9 papers
MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning
Ahmad Mousavi, Majid Alikhani, Yeon-Chang Lee +2
Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current architectures face two bottlenecks…
Anchored Alignment: Preventing Positional Collapse in Multimodal Recommender Systems
Yonghun Jeong, David Yoon Suk Kang, Yeon-Chang Lee
Multimodal recommender systems (MMRS) leverage images, text, and interaction signals to enrich item representations. However, recent alignment based MMRSs that enforce a unified em…
Embedding-aware Polarization Management in Signed Networks
Jeonghan Son, Kyungsik Han, Yeon-Chang Lee
Signed network embeddings (SNE) are widely used to represent networks with positive and negative relations, but their repeated use in downstream analysis pipelines can inadvertentl…
E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications
Jiwoo Kang, Yeon-Chang Lee
Multimodal recommender systems (MMRSs) enhance collaborative filtering by leveraging item-side modalities, but their reliance on a fixed set of modalities and task-specific objecti…
Improving the Accuracy of Community Detection on Signed Networks via Community Refinement and Contrastive Learning
Hyunuk Shin, Hojin Kim, Chanyoung Lee +2
Community detection (CD) on signed networks is crucial for understanding how positive and negative relations jointly shape network structure. However, existing CD methods often yie…
Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories
Yiqiao Jin, Andrew Zhao, Yeon-Chang Lee +3
We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent adv…
SVD-AE: Simple Autoencoders for Collaborative Filtering
Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee +2
Collaborative filtering (CF) methods for recommendation systems have been extensively researched, ranging from matrix factorization and autoencoder-based to graph filtering-based m…
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation
Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin +2
The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive…
A Survey on the Role of Crowds in Combating Online Misinformation: Annotators, Evaluators, and Creators
Bing He, Yibo Hu, Yeon-Chang Lee +3
Online misinformation poses a global risk with significant real-world consequences. To combat misinformation, current research relies on professionals like journalists and fact-che…