Our primary goal is to publish the papers in top-tier conferences related to the Web, information retrieval, and data mining. These conferences include WWW, ACM KDD, ACM SIGIR, ACM CIKM, IEEE ICDM, IEEE ICDE, and ACM WSDM.

Preprints

• [P02] Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories [ project page | code ]
  Yiqiao Jin, Andrew Zhao, Yeon-Chang Lee, Meng Ye, Ajay Divakaran, and Srijan Kumar
  arXiv:2406.17963

• [P01] A Survey on the Role of Crowds in Combating Online Misinformation: Annotators, Evaluators, and Creators [ project page ]
  Bing He, Yibo Hu, Yeon-Chang Lee, Soyoung Oh, Gaurav Verma, and Srijan Kumar
  arXiv:2310.02095

Conference & Journal Papers

• [C24] Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation [ code & data ]
  {Neng Kai Nigel Neo*, Yeon-Chang Lee*}, Yiqiao Jin, Sang-Wook Kim, and Srijan Kumar
  ACM CIKM 2024 (acceptance rate: 23.19%)

• [C23] PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed Networks
  Min-Jeong Kim, Yeon-Chang Lee, and Sang-Wook Kim
  ACM CIKM 2024 (acceptance rate: 23.19%)

• [C22] SVD-AE: Simple Autoencoders for Collaborative Filtering
  Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee, Srijan Kumar, and Noseong Park
  IJCAI 2024 (acceptance rate: TBA)

• [J09] A Survey of Graph Neural Networks for Social Recommender Systems [ project page ]
  {Kartik Sharma*, Yeon-Chang Lee*}, Sivagami Nambi, Aditya Salian, Shlok Shah, Sang-Wook Kim, and Srijan Kumar
  ACM Computing Surveys 2024 (SCIE journal, impact factor: 23.80)

• [J08] Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
  {Min-Jeong Kim*, Yeon-Chang Lee*}, David Y. Kang, and Sang-Wook Kim
  ACM TKDD 2024 (SCIE journal, impact factor: 4.00, short version [C19])

  • [J07] Learning to Compensate for Lack of Information: Extracting Latent Knowledge for Effective Temporal Knowledge Graph Completion
    {Yeon-Chang Lee*, JaeHyun Lee*}, Dongwon Lee, and Sang-Wook Kim
    Information Sciences 2024 (SCIE journal, impact factor: 8.23, short version [C16])

• [C21] 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: 24.04%)

• [C20] 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: 22.10%)

• [C19] 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: 25.12%; short papers track)

• [C18] 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: 19.21%; press release: NewsH, UNN, 교수신문)

• [C17] 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: 30.61%)

  • [J06] A Framework for Accurate Community Detection on Signed Networks Using Adversarial Learning
    Yoonsuk Kang, Woncheol Lee, Yeon-Chang Lee, Kyungsik Han, and Sang-Wook Kim
    IEEE TKDE 2023 (SCIE journal, impact factor: 9.23, short version [C10])

• [C16] 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: 19.66%)

• [C15] 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: 23.32%)

• [C14] 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: 29.04%; short papers track)

• [C13] 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: 28.57%; industry and applications track)

• [C12] 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: 20.23%)

• [C11] 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: 20.23%)

  • [J05] Effective and Efficient Negative Sampling in Metric Learning based Recommendation
    {Junha Park*, Yeon-Chang Lee*}, and Sang-Wook Kim
    Information Sciences 2022 (SCIE journal, impact factor: 8.23)

• [C10] 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: 20.00%)

• [C09] 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: 20.97%)

  • [J04] Exploiting Uninteresting Items for Effective Graph-Based One-Class Collaborative Filtering
    Yeon-Chang Lee, Jiwon Son, Taeho Kim, Daeyoung Park, and Sang-Wook Kim
    The Journal of Supercomputing 2021 (SCIE journal, impact factor: 2.55, short version [C06])

  • [J03] M-BPR: A Novel Approach to Improving BPR for Recommendation with Multi-type Pair-wise Preferences
    {Yeon-Chang Lee*, Taeho Kim*}, Jaeho Choi, Xiangnan He, and Sang-Wook Kim
    Information Sciences 2021 (SCIE journal, impact factor: 8.23)

• [C08] 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: 25.94%; short papers track)

• [C07] 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: 26.48%)

• [C06] Graph-Theoretic One-Class Collaborative Filtering using Signed Random Walk with Restart
  Yeon-Chang Lee, Daeyoung Park, Jiwon Son, Taeho Kim, and Sang-Wook Kim
  IEEE BigComp 2020 (short papers track; selected for journal invitation [J04])

• [C05] 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: 26.80%)

  • [J02] CrowdStart: Warming up Cold-Start Items using Crowdsourcing [ project page ]
    {Dong-Gyun Hong*, Yeon-Chang Lee*}, Jongwuk Lee, and Sang-Wook Kim
    Expert Systems with Applications 2019 (SCIE journal, impact factor: 8.66)

• [C04] 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: 24.55%)

• [C03] Exploiting Job Transition Patterns for Effective Job Recommendation
  {Yujin Lee*, Yeon-Chang Lee*}, Jiwon Hong, and Sang-Wook Kim
  IEEE SMC 2017

• [C02] Recommendation of Research Papers in DBpia: A Hybrid Approach Exploiting Content and Collaborative Data
  Yeon-Chang Lee, Jungwan Yeom, Kiburm Song, Jiwoon Ha, Kichun Lee, Jangho Yeo, and Sang-Wook Kim
  IEEE SMC 2016 (press release: 매일경제, 전자신문, UNN)

• [C01] Job Recommendation in AskStory: Experiences, Methods, and Evaluation
  Yeon-Chang Lee, Jiwon Hong, and Sang-Wook Kim
  ACM SAC 2016 (acceptance rate: 24.07%)

  • [J01] Improving the Accuracy of Top-N Recommendation using a Preference Model
    Jongwuk Lee, Dongwon Lee, Yeon-Chang Lee, Won-Seok Hwang, and Sang-Wook Kim
    Information Sciences 2016 (SCIE journal, impact factor: 8.23)

Posters & Newsletters

• [O02] Uninteresting Items: Concept and Its Application to Effective Collaborative Filtering in Recommender Systems
  Yeon-Chang Lee and Sang-Wook Kim
  ACM SIGWEB Newsletter 2023 (autumn issue)

• [O01] On Recommending Job Openings
  Yeon-Chang Lee, Jiwon Hong, Sang-Wook Kim, Sheng Gao, and Ji-Yong Hwang
  ACM HT 2015 (poster track)