SIGIR 논문을 낸 국내 연구실
탑티어이 학회에 논문을 낸 연구실
고려대 장부루 교수신생 랩모집 안내
탑티어 10전체 13구성원 12
대규모 언어 모델AI 안전·정렬
| 연구실 | 대학 | 탑티어 논문 | 전체 논문 | 구성원 | 비교 |
|---|---|---|---|---|---|
 데이터 인공지능 연구실유재민 교수신생 랩모집 안내 | 서울대 | 18 | 27 | – | |
 정보 및 지능 시스템 연구실이지형 교수모집 안내 | 성균관대 | 21 | 35 | – | |
 Data Intelligence and Learning Lab권원빈 교수신생 랩 | 성균관대 | 16 | 18 | – | |
 Data Intelligence Lab. 데이터 인텔리전스 연구실이연창 교수신생 랩 | UNIST | 12 | 21 | 15 | |
 Data Science and Artificial Intelligence Lab한진영 교수 | 성균관대 | 21 | 45 | – | |
 Financial Engineering Lab. 금융공학연구실이용재 교수 | UNIST | 3 | 26 | – | |
 언어 및 지능 연구실, Language and Intelligence Laboratory장부루 교수신생 랩모집 안내 | 고려대 | 10 | 13 | 12 | |
 Learning and Mining from Multimodal DAtaset(LAMDA) Lab오하영 교수모집 안내 | 성균관대 | 6 | 30 | 29 | |
 Natural Language Processing Lab김학수 교수모집 안내 | KAIST | 10 | 32 | – |
최근 논문
- ACE: Anisotropy-Controllable Embedding for LLM-enhanced Sequential Recommendation
2026, 데이터지능 및 학습 연구실
- Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
2026, Data Intelligence Lab, 데이터과학 및 응용 연구실 (Intelligent Data Science and Applications Lab)
- FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation
2026, Data Intelligence Lab, 데이터과학 및 응용 연구실 (Intelligent Data Science and Applications Lab)
- From Raw Features to Effective Embeddings: A Three-Stage Approach for Multimodal Recipe Recommendation
2026, 신기정 연구실
- IPQA: A Benchmark for Core Intent Identification in Personalized Question Answering
2026, 데이터인텔리전스
- ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation
2026, 신기정 연구실, 데이터 인공지능 연구실
- KCC: Korean Civil Case Dataset for Legal Information Retrieval
2026, Data Science and Artificial Intelligence Lab
- Mine over Yours: How Authorship Biases Evaluation in Generative Information Retrieval
2026, Human Centered Computing Lab(HCCL)
- MUDY: Multi-Granular Dynamic Candidate Contextualization for Unsupervised Keyphrase Extraction
2026, Data and Adaptive Intelligence Systems Lab
- MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender
2026, 데이터인텔리전스, 언어 및 범용인공지능, 데이터과학 및 응용 연구실 (Intelligent Data Science and Applications Lab)
- PEARL: Profile-based Explainable Agent for Edge LLM Recommendation via Latent Decomposition
2026, Learning and Mining from Multimodal DAtaset(LAMDA) Lab
- SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
2026, Data Intelligence and Learning Lab, Data and Adaptive Intelligence Systems Lab, 데이터과학 및 응용 연구실 (Intelligent Data Science and Applications Lab)
- Who Is Shopping With You? How Persona Design Shapes Cognitive and Social Engagement in AI Shopping Agents
2026, Human Centered Computing Lab(HCCL)
- COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation
2025, 빅데이터사이언스 연구실
- Conversational Argument Search Under Selective Exposure: Strategies for Balanced Perspective Access
2025, Human Centered Computing Lab(HCCL)
- DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation
2025, 데이터지능 및 학습 연구실
- Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios
2025, 박찬영 연구실
- Dynamic Time-aware Continual User Representation Learning
2025, 박찬영 연구실
- Embracing Plasticity: Balancing Stability and Plasticity in Continual Recommender Systems
2025, 데이터과학 및 응용 연구실 (Intelligent Data Science and Applications Lab)
- Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
2025, SLP Lab.(Speech and Language Processing Lab), Visual Information Processing Lab