CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship [ appendix | code | slides ]
Yeon-Chang Lee, JaeHyun Lee, Michiharu Yamashita, Dongwon Lee, and Sang-Wook Kim
ACM KDD 2025 (acceptance rate: 19% (Aug. Cycle))
CATER: A Cluster-Based Alternative-Term Recommendation Framework for Large-Scale Web Search at NAVER
Jiwon Son, Jaeyoon Kim, Taekin Kim, Yeon-Chang Lee, and Sang-Wook Kim
ACM KDD 2025 (acceptance rate: 22% (Aug. Cycle); applied data science track)
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks [ appendix | code | poster ]
Yeon-Chang Lee, Hojung Shin, and Sang-Wook Kim
AAAI 2025 (acceptance rate: 23%)
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation [ code & data | slides ]
{Neng Kai Nigel Neo*, Yeon-Chang Lee*}, Yiqiao Jin, Sang-Wook Kim, and Srijan Kumar
ACM CIKM 2024 (acceptance rate: 23%)
PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed Networks [ project page | code | slides ]
Min-Jeong Kim, Yeon-Chang Lee, and Sang-Wook Kim
ACM CIKM 2024 (acceptance rate: 23%)
SVD-AE: Simple Autoencoders for Collaborative Filtering [ code ]
Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee, Srijan Kumar, and Noseong Park
IJCAI 2024 (acceptance rate: 14%)
Representation Learning in Continuous-Time Dynamic Signed Networks [ code ]
{Kartik Sharma*, Mohit Raghavendra*}, Yeon-Chang Lee, Anand Kumar M, and Srijan Kumar
ACM CIKM 2023 (acceptance rate: 24%)
Predicting Information Pathways Across Online Communities [ code & data | video | slides ]
Yiqiao Jin, Yeon-Chang Lee, Kartik Sharma, Meng Ye, Karan Sikka, Ajay Divakaran, and Srijan Kumar
ACM KDD 2023 (acceptance rate: 22%)
TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks [ project page | code | poster ]
{Min-Jeong Kim*, Yeon-Chang Lee*}, and Sang-Wook Kim
ACM SIGIR 2023 (acceptance rate: 25%; short papers track)
Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding [ appendix | code | slides ]
Hyunsik Yoo, Yeon-Chang Lee, Kijung Shin, and Sang-Wook Kim
ACM Web Conference (WWW) 2023 (acceptance rate: 19%; press release: NewsH, UNN, 교수신문)
A Competition-Aware Approach to Accurate TV Show Recommendation [ code ]
Hong-Kyun Bae, Yeon-Chang Lee, Kyungsik Han, and Sang-Wook Kim
IEEE ICDE 2023 (acceptance rate: 31%)
THOR: Self-Supervised Temporal Knowledge Graph Embedding via Three-Tower Graph Convolutional Networks [ project page | code | slides ]
{Yeon-Chang Lee*, JaeHyun Lee*}, Dongwon Lee, and Sang-Wook Kim
IEEE ICDM 2022 (acceptance rate: 20%)
MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation [ project page | slides ]
{Taeri Kim*, Yeon-Chang Lee*}, Kijung Shin, and Sang-Wook Kim
ACM CIKM 2022 (acceptance rate: 23%)
Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation
{Taeho Kim*, Yungi Kim*}, Yeon-Chang Lee, Won-Yong Shin, and Sang-Wook Kim
ACM CIKM 2022 (acceptance rate: 29%; short papers track)
AiRS: A Large-Scale Recommender System at NAVER News [ project page | slides ]
{Hongjun Lim*, Yeon-Chang Lee*}, Jin-Seo Lee, Sanggyu Han, Seunghyeon Kim, Yeongjong Jeong, Changbong Kim, Jaehun Kim, Sunghoon Han, Solbi Choi, Hanjong Ko, Dokyeong Lee, Jaeho Choi, Yungi Kim, Hong-Kyun Bae, Taeho Kim, Jeewon Ahn, Hyun-Soung You, and Sang-Wook Kim
IEEE ICDE 2022 (acceptance rate: 28%; industry and applications track)
Directed Network Embedding with Virtual Negative Edges [ project page | code | slides ]
{Hyunsik Yoo*, Yeon-Chang Lee*}, Kijung Shin, and Sang-Wook Kim
ACM WSDM 2022 (acceptance rate: 20%)
Linear, or Non-Linear, That is the Question! [ code ]
{Taeyong Kong*, Taeri Kim*}, Jinsung Jeon, Jeongwhan Choi, Yeon-Chang Lee, Noseong Park, and Sang-Wook Kim
ACM WSDM 2022 (acceptance rate: 20%)
Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks
{Yoonsuk Kang*, Woncheol Lee*}, Yeon-Chang Lee, Kyungsik Han, and Sang-Wook Kim
IEEE ICDM 2021 (acceptance rate: 20%)
Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks [ project page | code | slides ]
{Wonchang Lee*, Yeon-Chang Lee*}, Dongwon Lee, and Sang-Wook Kim
ACM SIGIR 2021 (acceptance rate: 21%)
Are Negative Links Really Beneficial to Network Embedding? In-Depth Analysis and Interesting Results [ project page | slides ]
Yeon-Chang Lee, Nayoun Seo, and Sang-Wook Kim
ACM CIKM 2020 (acceptance rate: 26%; short papers track)
ASiNE: Adversarial Signed Network Embedding [ project page | code | slides ]
Yeon-Chang Lee, Nayoun Seo, Kyungsik Han, and Sang-Wook Kim
ACM SIGIR 2020 (acceptance rate: 26%)
No, That’s Not My Feedback: TV Show Recommendation Using Watchable Interval [ slides ]
{Kyung-Jae Cho*, Yeon-Chang Lee*}, Kyungsik Han, Jaeho Choi, and Sang-Wook Kim
IEEE ICDE 2019 (acceptance rate: 27%)
gOCCF: Graph-Theoretic One-Class Collaborative Filtering Based on Uninteresting Items [ project page | code | slides ]
Yeon-Chang Lee, Sang-Wook Kim, and Dongwon Lee
AAAI 2018 (acceptance rate: 25%)