Publication

SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation [link]

Gyuseok Lee, Wonbin Kweon, Zhenrui Yue, Yaokun Liu, Yifan Liu, Susik Yoon, Dong Wang, SeongKu Kang

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2026

• We propose a scalable SBR framework that incorporates reliable and informative intents 

Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders [link]

Jaehyun Lee, Sanghwan Jang, SeongKu Kang†, Hwanjo Yu†

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2026

• We estimate LLM's internal knowledge and selectively inject additional information 

FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation [link]

WooJoo Kim, Junyoung Kim, Jaehyung Lim, Seong Jin Choi, SeongKu Kang†, Hwanjo Yu†

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2026

• We condense ensemble-level diversity into a single network for sequential recommendation

MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender [link]

Tongyoung Kim, Soojin Yoon, SeongKu Kang, Jinyoung Yeo, Dongha Lee

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2026

• We integrate various prompt template-index combinations with a unified variational framework

Continual Low-Rank Adapters for LLM-based Generative Recommender Systems [link]

Hyunsik Yoo, Ting-Wei Li, SeongKu Kang, Zhining Liu, Charlie Xu, Qilin Qi, Hanghang Tong

International Conference on Learning Representations (ICLR), 2026

• We adapt LoRA continuously for recommendation

Continual Recommender Systems: A Focus on LLMs and Evolving Trends

Seunghan Lee, Seunghyun Baek, Dojun Hwang, Hyunsik Yoo, SeongKu Kang

International Conference on Database Systems for Advanced Applications (DASFAA), Tutorial, 2026

• We present a tutorial on continual learning with a focus on LLMs. Tutorial page: link 

Capturing User Interests from Data Streams for Continual Sequential Recommendation [link]

Gyuseok Lee, Hyunsik Yoo, Junyoung Hwang, SeongKu Kang†, Hwanjo Yu

ACM International Conference on Web Search and Data Mining (WSDM), 2026

• We propose CSTRec which continuously updates a transformer-based SR model

PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval [link]

Wonbin Kweon, Runchu Tian, SeongKu Kang, Pengcheng Jiang, Zhiyong Lu, Jiawei Han, Hwanjo Yu

ACM The Web Conference (WWW), 2026

• We match scientific document semantics using entity-aspect pairs

CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory [link]

HuiJeong Son, Hyeongu Kang, Sunho Kim, Subeen Ho, SeongKu Kang, Dongha Lee, Susik Yoon

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026

• We retrieve from streaming corpora with adaptive memory

BPL: Bias-adaptive Preference Distillation Learning for Recommender System [link]

SeongKu Kang, Jianxun Lian, Dongha Lee, Wonbin Kweon, Sanghwan Jang, Jaehyun Lee, Jindong Wang, Xing Xie, Hwanjo Yu

IEEE Transactions on Knowledge and Data Engineering (TKDE), SCIE (Q1: IF Top 1.8%), 2026

• We learn user preferences with bias-adaptive distillation

Topic Coverage-based Demonstration Retrieval for In-Context Learning [link]

Wonbin Kweon, SeongKu Kang, Runchu Tian, Pengcheng Jiang, Jiawei Han, Hwanjo Yu

Conference on Empirical Methods in Natural Language Processing (EMNLP), Main, 2025

• We select in-context demonstrations based on topic coverage

Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking [link]

Yunyi Zhang, Ruozhen Yang, Siqi Jiao, SeongKu Kang, Jiawei Han

Conference on Empirical Methods in Natural Language Processing (EMNLP), Findings, 2025

• We rank scientific papers using LLM-guided semantics

Continual Recommender Systems [link]

Hyunsik Yoo, SeongKu Kang, Hanghang Tong

ACM International Conference on Information and Knowledge Management (CIKM), Tutorial, 2025

• We present a tutorial on continual learning for recommender systems. Tutorial page: link 

DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning [link]

Pengcheng Jiang, Jiacheng Lin, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, Jiawei Han

Conference on Language Modeling (COLM), 2025

• We propose DeepRetrieval, an RL-based framework for training LLMs to enhance retrieval

Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval [link]

Sangam Lee, Ryang Heo, SeongKu Kang, Dongha Lee

Conference on Language Modeling (COLM), 2025

• We index documents as scenario-based retrieval units

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark [link]

Suyeon Kim, SeongKu Kang†, Dongwoo Kim, Jungseul Ok, Hwanjo Yu†

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025

• We propose a new benchmark that provides realistic graph datasets with various noises

Embracing Plasticity: Balancing Stability and Plasticity in Continual Recommender Systems [link]

Hyunsik Yoo, SeongKu Kang, Ruizhong Qiu, Charlie Xu, Fei Wang and Hanghang Tong

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2025

• We adaptively balance stability and plasticity based on user preference shifts

Personalized Preference Reasoning with Large Language Models for Accurate and Explainable Recommendation [link]

Jieyong Kim, Hyunseo Kim, Hyunjin Cho, SeongKu Kang, Buru Chang, Jinyoung Yeo and Dongha Lee

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2025

• We reason over user preferences with LLMs for recommendation

Uncertainty Quantification and Decomposition for LLM-based Recommendation [link]

Wonbin Kweon, Sanghwan Jang, SeongKu Kang†, Hwanjo Yu†

ACM The Web Conference (WWW), 2025

• We quantify and decompose the uncertainty in LLM-based recommendation

Chain-of-Factors Paper-Reviewer Matching [link]

Yu Zhang, Yanzhen Shen, SeongKu Kang, Xiusi Chen, Bowen Jin, Jiawei Han 

ACM The Web Conference (WWW), 2025

• We match papers and reviewers using semantic, topic, and citation signals

Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation [link]

SeongKu Kang, Bowen Jin, Wonbin Kweon, Yu Zhang, Dongha Lee, Jiawei Han, Hwanjo Yu 

ACM International Conference on Web Search and Data Mining (WSDM), 2025

• We generates queries with comprehensive coverage of a document's concepts.

• Best Papers of WSDM 2025, Invited to ACM TIST.

Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts [link]

Soojin Yoon, Sungho Ko, Tongyoung Kim, SeongKu Kang, Jinyoung Yeo, Dongha Lee 

ACM International Conference on Web Search and Data Mining (WSDM), 2025

• We propose an unsupervised cross-lingual entity alignment pipeline

Taxonomy-guided Semantic Indexing for Academic Paper Search [link]

SeongKu Kang, Yunyi Zhang, Pengcheng Jiang, Dongha Lee, Jiawei Han, Hwanjo Yu 

Conference on Empirical Methods in Natural Language Processing (EMNLP), Main (oral), 2024

• We index scientific concepts using taxonomy-guided semantics 

Continual Collaborative Distillation for Recommender System [link]

Gyuseok Lee*, SeongKu Kang*, Wonbin Kweon, Hwanjo Yu 

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024

• We combine distillation and continual learning for recommendation 

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset [link]

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

• We build a persona- and knowledge-grounded conversational recommendation dataset 

Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy [link]

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

• We predict aspect-sentiment with reasoning and extraction 

Multi-Domain Sequential Recommendation via Domain Space Learning [link]

Junyoung Hwang, Hyunjun Ju, SeongKu Kang, Sanghwan Jang, Hwanjo Yu

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2024

• We model multi-domain sequential recommendation under sparse interactions 

Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection [link]

Suyeon Kim, Dongha Lee, SeongKu Kang, Sukang Chae, Sanghwan Jang, Hwanjo Yu

Conference on Computer Vision and Pattern Recognition (CVPR), 2024

• We distinguishe mislabeled instances based on the dynamics of the training signals

Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems [link]

SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu

ACM Transactions on Recommender Systems (TORS), 2024

• We leverage dissensus of models to mitigate the popularity amplifications

Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy [link]

SeongKu Kang, Shivam Agarwal, Bowen Jin, Dongha Lee, Hwanjo Yu, Jiawei Han

ACM The Web Conference (WWW), 2024

• We improve retrieval using a corpus-level topical taxonomy 

Top-Personalized-K Recommendation [link]

Wonbin Kweon, SeongKu Kang, Sanghwan Jang, Hwanjo Yu

ACM The Web Conference (WWW), 2024

• We generate a personalized-sized ranking list to maximize user satisfaction

Multi-Domain Recommendation to Attract Users via Domain Preference Modeling [link]

Hyunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang, Hwanjo Yu

AAAI Conference on Artificial Intelligence (AAAI), 2024

• We learn various seen-unseen domain mappings with masked domain modeling

MvFS: Multi-view Feature Selection for Recommender System [link]

Youngjune Lee, Yeongjong Jeong, Keunchan Park, SeongKu Kang†

ACM International Conference on Information and Knowledge Management (CIKM), Short paper, 2023

• We promote balanced feature selection while mitigating bias toward dominant patterns

Distillation from Heterogeneous Models for Top-K Recommendation [link]

SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu

ACM The Web Conference (WWW), 2023

• We compress ensemble of heterogeneous models, reducing latency while retaining accuracy

Learning Topology-Specific Experts for Molecular Property Prediction [link]

Suyeon Kim, Dongha Lee, SeongKu Kang, Seonghyeon Lee, Hwanjo Yu

AAAI Conference on Artificial Intelligence (AAAI), 2023

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

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering [link]

SeongKu Kang, Dongha Lee, Wonbin Kweon, Junyoung Hwang, Hwanjo Yu

ACM The Web Conference (WWW), 2022

• We exploit the complementarity from heterogeneous objectives for collaborative filtering

TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters [link]

Dongha Lee, Jiaming Shen, SeongKu Kang, Susik Yoon, Jiawei Han, Hwanjo Yu

ACM The Web Conference (WWW), 2022

• We recursively expand the taxonomy by discovering novel clusters of terms and documents

Obtaining Calibrated Probabilities with Personalized Ranking Models [link]

Wonbin Kweon, SeongKu Kang, Hwanjo Yu

AAAI Conference on Artificial Intelligence (AAAI), Oral, 2022.

• 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 [link]

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 [link]

SeongKu Kang, Dongha Lee, Wonbin Kweon, Hwanjo Yu

Knowledge-Based Systems (SCI), 2022

• We distill latent knowledge in a balanced way without relying on any hyperparameter

Topology Distillation for Recommender System [link]

SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu
ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD), 2021

• We transfer the topological structure built upon the relations in the teacher space

Bootstrapping User and Item Representations for One-Class Collaborative Filtering [link]

Dongha Lee, SeongKu Kang, Hyunjun Ju, Chanyoung Park, Hwanjo Yu
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021

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

Unsupervised Proxy Selection for Session-based Recommender Systems [link]

Junsu Cho, SeongKu Kang, Dongmin Hyun, Hwanjo Yu
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021

• We imitate the missing information of user interest by modeling proxies of sessions

Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation [link]

Junsu Cho, Dongmin Hyun, SeongKu Kang, Hwanjo Yu

ACM International World-Wide Web Conference (WWW), 2021

• We exploits heterogeneous temporal patterns of user preference

Bidirectional Distillation for Top-K Recommender System [link]

Wonbin Kweon, SeongKu Kang, Hwanjo Yu

ACM International World-Wide Web Conference (WWW), 2021

• We improve teacher and student collaboratively via bidirectional distillation

Item-side Ranking Regularized Distillation for Recommender System [link]

SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu

Information Sciences (SCI), 2021

• We propose a new regularization method designed to enhance ranking distillation.

DE-RRD: A Knowledge Distillation Framework for Recommender System [link]

SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu

ACM International Conference on Information and Knowledge Management (CIKM), 2020

• We propose (1) DE for latent knowledge distillation, (2) RRD for ranking knowledge distillation

Deep Rating Elicitation for New Users in Collaborative Filtering [link]

Wonbin Kweon, SeongKu Kang, Junyoung Hwang , Hwanjo Yu

ACM International World-Wide Web Conference (WWW), Short paper, 2020

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

Multi-Modal Component Embedding for Fake News Detection [link]

SeongKu Kang, Junyoung Hwang , Hwanjo Yu

IEEE International Conf. Ubiquitous Information Management and Communication (IMCOM), 2020

• We explore the multi-modal feature combination for fake news detection

Semi-Supervised Learning for Cross-Domain Recommendation to Cold-start Users [link]

SeongKu Kang, Junyoung Hwang, Dongha Lee, Hwanjo Yu

ACM International Conference on Information and Knowledge Management (CIKM), 2019

• We introduce a semi-supervised mapping when the overlapping users are exteremly limited.

• Ranked 12th among the most influential papers at CIKM 2019 (link) 

Densifying a Trust Network for Effective Collaborative Filtering [link]

SeongKu Kang, Jemin Wang, Yeon-Chang Lee, Sang-Wook Kim

Korean DataBase Conference (KDBC), 🏆 Best Paper Award, 2017.

• We densify social network to provide supplementary signals for recommendation