News

hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising

PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval

Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation

SCoRD: Semantic-Assisted Continual Retriever-Reranker Distillation for LLM-Based Recommendation

Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders

Dynamic Spectral Decoupling for Time-Series Forecasting

TRACER: Balancing Stability-Plasticity-Cognitivity Trilemma for LLM Enhanced Continual Recommendation

Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization

Improving Scientific Document Retrieval with Academic Concept Index

Prof. Kang received the Seoktop Lecture Award (Big Data Analytics) from Korea University.

CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrieval

Invited talk: “Knowledge-Structured Retrieval for Scientific Literature”, Data Intelligence Workshop, 2026.

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

Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders

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

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

Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths

Invited talk: “Knowledge-Structured Retrieval for Scientific Literature”, CSE/GSAI Seminar, POSTECH, 2026.

Continual Low-Rank Adapters for LLM-based Generative Recommender Systems

Continual Recommender Systems: A Focus on LLMs and Evolving Trends

Capturing User Interests from Data Streams for Continual Sequential Recommendation

PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Retrieval

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

BPL: Bias-adaptive Preference Distillation Learning for Recommender System

Prof. Kang was invited as an Area Chair of KDD and the EMNLP System Demonstration Track, and to program committees including AAAI, SIGIR, NeurIPS, CIKM, ICDM, SIGIR-AP, and ACM AI Summit.

Seunghan was selected as a recipient of the AI SeoulTech Scholarship!

Prof. Kang received the Seoktop Lecture Award (Artificial Intelligence) from Korea University.

Prof. Kang was invited as a PhD Mentor for the CIKM 2025 PhD Symposium.

Our scientific document retrieval paper was selected as one of the Best Papers of WSDM 2025 and invited to ACM TIST.

Topic Coverage-based Demonstration Retrieval for In-Context Learning

Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking

Continual Recommender Systems

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

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

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

Embracing Plasticity: Balancing Stability and Plasticity in Continual Recommender Systems

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

Uncertainty Quantification and Decomposition for LLM-based Recommendation

Chain-of-Factors Paper-Reviewer Matching

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

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

Prof. Kang was invited to program committees including KDD, WWW, AAAI, SIGIR, SIGIR-AP, ACL, AACL-SRW, CVPR, and DASFAA.