14 papers
Improving Scientific Document Retrieval with Academic Concept Index
Jeyun Lee, Junhyoung Lee, Wonbin Kweon +7
Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations and the substantial mismatch in voc…
MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender
Tongyoung Kim, Soojin Yoon, SeongKu Kang +2
Language Models (LMs) have been widely used in recommender systems to incorporate textual information of items into item IDs, leveraging their advanced language understanding and g…
Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
Jaehyun Lee, Sanghwan Jang, SeongKu Kang +1
Large language models (LLMs) have recently emerged as powerful training-free recommenders. However, their knowledge of individual items is inevitably uneven due to imbalanced infor…
Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths
Sangam Lee, Ryang Heo, SeongKu Kang +3
Generative retrieval directly decode a document identifier (i.e., docid) in response to a query, making it impossible to provide users with explanations as an answer for ``why is t…
CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory
HuiJeong Son, Hyeongu Kang, Sunho Kim +4
Information retrieval (IR) in dynamic data streams is a crucial task, as shifts in data distribution degrade the performance of AI-powered IR systems. To mitigate this issue, memor…
Capturing User Interests from Data Streams for Continual Sequential Recommendation
Gyuseok Lee, Hyunsik Yoo, Junyoung Hwang +2
Transformer-based sequential recommendation (SR) models excel at modeling long-range dependencies in user behavior via self-attention. However, updating them with continuously arri…