About Seminar

In this seminar, we discuss diverse research topics such as data mining, graph machine learning, and applied data science including recommender systems. Here are the details of the seminar.

• Time: Every thursday
• Place: 숭실대학교 정보과학관 301호

Seminar Information

2025

Date	Title	Speaker	Slide
25/04/03	Upcoming G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation	Jaehyun Park	[link]
25/03/20	DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation	Sunuk Kim	[link]
25/03/10	Content-Based Collaborative Generation for Recommender Systems	Jiseung Hyun	[link]
25/02/20	CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation	Jaehyun Park	[link]
25/02/13	Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System	Jiseung Hyun	[link]
25/02/06	Personalized Denoising Implicit Feedback for Robust Recommender System	Minseo Jeon	[link]
25/01/23	Disentangled Graph Collaborative Filtering	Sunuk Kim	[link]
25/01/16	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs	Daewon Gwak	[link]
25/01/09	Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond	Junwoo Jung	[link]
25/01/02	BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart	Jinhong Jung	[link]

2024

Date	Title	Speaker	Slide
24/12/02	Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation	Sunuk Kim	[link]
24/11/25	Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks (KDD’24)	Jiseung Hyun	[link]
24/11/11	RecExplainer: Aligning Large Language Models for Explaining Recommendation Models	Seokhyeon Cho	[link]
24/11/04	Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback	Junwoo Jung	[link]
24/10/14	Self-Guided Learning to Denoise for Robust Recommendation	Minseo Jeon	[link]
24/10/07	MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation	Geonwoo Ko	[link]
24/09/30	Hypergraph Neural Networks	Daewon Gwak	[link]
24/06/20	Dvine : Directed Network Embedding with virtual Negative Edges	Minseo Jeon	[link]
24/05/30	Predict Then Propagate: Graph Neural Networks Meet Personalized Pagerank	Daewon Gwak	[link]
24/05/23	LightGCL: Simple yet effective graph contrastive learning for recommendation	Cheolhee Jeong	[link]
24/05/09	Recommender Systems with Generative Retrieval	Junwoo Jung	[link]
24/05/02	Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks	Seokhyeon Cho	[link]
24/04/19	Automated Self Supervised Learning for Recommendation	Jongyoon Choi	[link]
24/04/04	LLMRec: Large Language Models with Graph Augmentation for Recommendation	Geonwoo Ko	[link]
24/03/28	Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning	Jinhong Jung	[link]