8 papers
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…
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…
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…