Yeon-Chang Lee

Orcid: 0000-0002-8769-0678

According to our database1, Yeon-Chang Lee authored at least 40 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A Survey of Graph Neural Networks for Social Recommender Systems.
ACM Comput. Surv., October, 2024

Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion.
Inf. Sci., January, 2024

CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship.
CoRR, 2024

Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks.
CoRR, 2024

Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories.
CoRR, 2024

Towards Fair Graph Anomaly Detection: Problem, New Datasets, and Evaluation.
CoRR, 2024

SVD-AE: Simple Autoencoders for Collaborative Filtering.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed Networks.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023
A Framework for Accurate Community Detection on Signed Networks Using Adversarial Learning.
IEEE Trans. Knowl. Data Eng., November, 2023

Uninteresting Items: Concept and Its Application to Effective Collaborative Filtering in Recommender Systems.
SIGWEB Newsl., 2023

A Survey on the Role of Crowds in Combating Online Misinformation: Annotators, Evaluators, and Creators.
CoRR, 2023

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding.
CoRR, 2023

Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding.
Proceedings of the ACM Web Conference 2023, 2023

TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks.
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Predicting Information Pathways Across Online Communities.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

A Competition-Aware Approach to Accurate TV Show Recommendation.
Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Representation Learning in Continuous-Time Dynamic Signed Networks.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022
Effective and efficient negative sampling in metric learning based recommendation.
Inf. Sci., 2022

Directed Network Embedding with Virtual Negative Edges.
Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

Linear, or Non-Linear, That is the Question!
Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

THOR: Self-Supervised Temporal Knowledge Graph Embedding via Three-Tower Graph Convolutional Networks.
Proceedings of the IEEE International Conference on Data Mining, 2022

AiRS: A Large-Scale Recommender System at NAVER News.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021
Exploiting uninteresting items for effective graph-based one-class collaborative filtering.
J. Supercomput., 2021

M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences.
Inf. Sci., 2021

Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks.
Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks.
Proceedings of the IEEE International Conference on Data Mining, 2021

2020
ASiNE: Adversarial Signed Network Embedding.
Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

Are Negative Links Really Beneficial to Network Embedding?: In-Depth Analysis and Interesting Results.
Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

Graph-Theoretic One-Class Collaborative Filtering using Signed Random Walk with Restart.
Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing, 2020

2019
CrowdStart: Warming up cold-start items using crowdsourcing.
Expert Syst. Appl., 2019

No, That's Not My Feedback: TV Show Recommendation Using Watchable Interval.
Proceedings of the 35th IEEE International Conference on Data Engineering, 2019

2018
gOCCF: Graph-Theoretic One-Class Collaborative Filtering Based on Uninteresting Items.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Exploiting job transition patterns for effective job recommendation.
Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, 2017

2016
Improving the accuracy of top-N recommendation using a preference model.
Inf. Sci., 2016

Recommendation of research papers in DBpia: A Hybrid approach exploiting content and collaborative data.
Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016

Job recommendation in AskStory: experiences, methods, and evaluation.
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016

2015
On Recommending Job Openings.
Proceedings of the 26th ACM Conference on Hypertext & Social Media, 2015


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