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SeongKu Kang

I am a postdoctoral researcher in the Department of Computer Science at UIUC, working with Prof. Jiawei Han. I earned my Ph.D. degree at POSTECH, where I was fortunate to be advised by Prof. Hwanjo Yu. During my Ph.D., I interned at Microsoft Research Asia in the Social Computing group. Feel free to send me an email if you are interested in collaborating with me!

Email  |  CV  |  Google Scholar  |  Github


  • Data mining and machine learning for real-world applications
  • Information retrieval and recommender systems

Unless otherwise specified, the paper is accepted as a research track long/regular paper. * indicates equal contribution.
  Preprint
Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks
Jaehyun Lee*, SeongKu Kang*, Hwanjo Yu
Arxiv, 2024.
  2024
Continual Collaborative Distillation for Recommender System
Gyuseok Lee*, SeongKu Kang*, Wonbin Kweon, Hwanjo Yu
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024.
pdf / code

We introduce a new research direction that combines knowledge distillation and continual learning for practical recommender systems.

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Minjin Kim*, Minju Kim*, Hana Kim, Beong-woo Kwak, Soyeon Chun, Hyunseo Kim, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee
Annual Meeting of the Association for Computational Linguistics (ACL), Findings, 2024.
pdf / code

We present a new conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators.

Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy
Jieyong Kim*, Ryang Heo*, Yongsik Seo, SeongKu Kang, Jinyoung Yeo, Dongha Lee
Annual Meeting of the Association for Computational Linguistics (ACL), short paper, Findings, 2024.
pdf / code

We propose SCRAP which optimizes its model to generate reasonings and the corresponding sentiment quadruplets in sequence.

Multi-Domain Sequential Recommendation via Domain Space Learning
Junyoung Hwang, Hyunjun Ju, SeongKu Kang, Sanghwan Jang, Hwanjo Yu
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2024.
pdf / code

We introduce a new multi-domain sequential recommendation method, specifically targeting the challenging scenario where recent interactions are highly sparse.

Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
Suyeon Kim, Dongha Lee, SeongKu Kang, Sukang Chae, Sanghwan Jang, Hwanjo Yu
Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
pdf / code

We propose DynaCor that distinguishes mislabeled instances based on the dynamics of the training signals.

Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems
SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu
ACM Transactions on Recommender Systems (TORS), 2024.

We introduce a new approach to leverage dissensus of models to mitigate the popularity amplifications of a large-scale RS.

Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
SeongKu Kang, Shivam Agarwal, Bowen Jin, Dongha Lee, Hwanjo Yu, Jiawei Han
ACM The Web Conference (WWW), 2024.
pdf / code

We introduce a new plug-and-play ToTER framework which improves PLM-based retrieval using a corpus topical taxonomy.

Top-Personalized-K Recommendation
Wonbin Kweon, SeongKu Kang, Sanghwan Jang, Hwanjo Yu
ACM The Web Conference (WWW), 2024.
pdf / code

We propose Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction.

Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Hyunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang, Hwanjo Yu
AAAI Conference on Artificial Intelligence (AAAI), 2024.
pdf / code

We propose a new multi-domain recommendation framework that learns various seen-unseen domain mappings in a unified way with masked domain modeling.

  2023
MvFS: Multi-view Feature Selection for Recommender System
Youngjune Lee, Keunchan Park, Yeongjong Jeong, SeongKu Kang (corresponding author)
ACM International Conference on Information and Knowledge Management (CIKM), short paper, 2023.
pdf / code

We propose Multi-view Feature Selection (MvFS), which promotes more balanced feature selection while mitigating bias toward dominant patterns.

Distillation from Heterogeneous Models for Top-K Recommendation
SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu
ACM The Web Conference (WWW), 2023.
pdf / code

We propose a new framework that compresses ensemble of heterogeneous models, reducing huge inference costs while retaining high accuracy.

Learning Topology-Specific Experts for Molecular Property Prediction
Suyeon Kim, Dongha Lee, SeongKu Kang, Seonghyeon Lee, Hwanjo Yu
AAAI Conference on Artificial Intelligence (AAAI), 2023.
pdf / code

We introduce a new topology-based gating module for molecular property prediction.

  2022
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
SeongKu Kang, Dongha Lee, Wonbin Kweon, Junyoung Hwang, Hwanjo Yu
ACM The Web Conference (WWW), 2022.
pdf / code

We introduce a new training strategy that exploits the complementarity from heterogeneous objectives for one-class collaborative filtering.

TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters
Dongha Lee, Jiaming Shen, SeongKu Kang, Susik Yoon, Jiawei Han, Hwanjo Yu
ACM The Web Conference (WWW), 2022.
pdf / code

We introduce TaxoCom which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents.

Obtaining Calibrated Probabilities with Personalized Ranking Models
Wonbin Kweon, SeongKu Kang, Hwanjo Yu
AAAI Conference on Artificial Intelligence (AAAI), oral, 2022.
pdf / code

We propose two calibration methods for ranking model and a new unbiased empirical risk minimization framework to guide the calibration methods.

Mitigating viewpoint sensitivity of self-supervised one-class classifiers
Hyunjun Ju, Dongha Lee, SeongKu Kang, Hwanjo Yu
Information Sciences (SCI), 2022.

We propose GROC, a one-class classifier robust to geometrically-transformed inputs.

Personalized Knowledge Distillation for Recommender System
SeongKu Kang, Dongha Lee, Wonbin Kweon, Hwanjo Yu
Knowledge-Based Systems (SCI), 2022.

We introduce a new distillation strategy, distilling the preference knowledge in a balanced way without relying on any hyperparameter.

  2021
Topology Distillation for Recommender System
SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.
pdf / code

We introduce topology distillation approach that guides the student by transferring the topological structure built upon the relations in the teacher space.

Bootstrapping User and Item Representations for One-Class Collaborative Filtering
Dongha Lee, SeongKu Kang, Hyunjun Ju, Chanyoung Park, Hwanjo Yu
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021.
pdf / code

We propose BUIR, a new training framework that does not require negative sampling.

Unsupervised Proxy Selection for Session-based Recommender Systems
Junsu Cho, SeongKu Kang, Dongmin Hyun, Hwanjo Yu
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021.
pdf / code

We propose ProxySR which imitates the missing information of general user interest by modeling proxies of sessions.

Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
Junsu Cho, Dongmin Hyun, SeongKu Kang, Hwanjo Yu
ACM International World-Wide Web Conference (WWW), 2021.
pdf / code

We propose TimelyRec which exploits heterogeneous temporal patterns of user preference.

Bidirectional Distillation for Top-K Recommender System
Wonbin Kweon, SeongKu Kang, Hwanjo Yu
ACM International World-Wide Web Conference (WWW), 2021.
pdf / code

We introduce BD framework whereby both the teacher and the student collaboratively improve with each other.

Item-side Ranking Regularized Distillation for Recommender System
SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu
Information Sciences (SCI), 2021.

We propose a new regularization method designed to maximize the effect of the ranking distillation.

 2020
DE-RRD: A Knowledge Distillation Framework for Recommender System
SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu
ACM International Conference on Information and Knowledge Management (CIKM), 2020.
pdf / code

We propose two methods: (1) DE for latent knowledge distillation, (2) RRD for ranking knowledge distillation.

Deep Rating Elicitation for New Users in Collaborative Filtering
Wonbin Kweon, SeongKu Kang, Junyoung Hwang , Hwanjo Yu
ACM International World-Wide Web Conference (WWW), short paper, 2020.
pdf / code

We introduce DRE, a new framework to choose the initial seed items for new users.

Multi-Modal Component Embedding for Fake News Detection
SeongKu Kang, Junyoung Hwang , Hwanjo Yu
IEEE International Conf. Ubiquitous Information Management and Communication (IMCOM), 2020.

We propose a new method of multi-modal feature combination for fake news detection.

  2019 and Before
Semi-Supervised Learning for Cross-Domain Recommendation to Cold-start Users
SeongKu Kang, Junyoung Hwang, Dongha Lee, Hwanjo Yu
ACM International Conference on Information and Knowledge Management (CIKM), 2019.

We introduce a new semi-supervised learning solution that is effective when the overlapping users are exteremly limited.

Densifying a Trust Network for Effective Collaborative Filtering
SeongKu Kang, Jemin Wang, Yeon-Chang Lee, Sang-Wook Kim
Korean DataBase Conference (KDBC), Best Paper Award, 2017.

We propose a new method to densify social network for recommendation.


Postdoctoral Researcher | UIUC
Jun 2023 - Present

Research Intern | Microsoft Research Asia
May 2022 - April 2023


Ph.D in Computer Science and Engineering | POSTECH
Mar 2018 - Aug 2023

B.S in Computer Science and Engineering | Nanyang Technological University
Jul 2016 - May 2017

  • Exchange Student
B.S in Computer Science and Engineering | Hanyang University
Mar 2012 - Feb 2018

  • Cumulative GPA: 4.43/4.50 (ranked 1st in CSE Dept.)
  • Leave of absence for mandatory military service: Mar 2014 - Mar 2016

  • Stars of Tomorrow Award for Outstanding Intern, Microsoft Research Asia, 2023
  • POSTECHIAN Fellowship, POSTECH, 2022
  • 4th Rank @ POSTECH Research Award, National Research Foundation in Korea and POSTECH, 2022
  • Naver Ph.D Fellowship, Naver, 2021
  • 3rd Rank @ Music Playlist Recommendation Competition, Kakao, 2020
  • Student Travel Award, ACM SIGIR, 2019-2020
  • Dean’s List, Hanyang University, 2018
  • 3rd Rank @ Best Paper Award, Korean Database Conference (KDBC), 2017
  • Hanyang Brain Scholarship, Hanyang University, 2012
  • National Science and Technology Scholarship, Ministry of Education in Korea, 2012-2017

  • Hyundai AI Competition, Mentor, Hyundai Motors, 2022
  • Predictive Analytics Modelling (DS30), TA, Hyundai Motors, 2020-2022
  • Introduction to Computer SW Systems, TA, POSTECH, Fall 2021
  • Data Structure, TA, POSTECH, Fall 2019
  • Introduction to Computer Vision, TA, POSTECH, Fall 2018


Website templates from (1), (2) and (3).