13 papers
ACE: Anisotropy-Controllable Embedding for LLM-enhanced Sequential Recommendation
Dongcheol Lee, Hye-young Kim, Jongwuk Lee
Recent advances in the LLM-as-Extractor paradigm leverage large language models (LLMs) to transfer semantically rich item embeddings into sequential recommendation (SR) backbones.…
HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval
Sungho Park, Joohyung Yun, Jongwuk Lee +1
Table-text retrieval aims to retrieve relevant tables and text to support open-domain question answering. Existing studies use either early or late fusion, but face limitations. Ea…
MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation
Hyunsoo Kim, Jaewan Moon, Seongmin Park +1
Modern recommender systems trained on domain-specific data often struggle to generalize across multiple domains. Cross-domain sequential recommendation has emerged as a promising r…
Enhancing Time Awareness in Generative Recommendation
Sunkyung Lee, Seongmin Park, Jonghyo Kim +2
Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large langua…
LLM-Enhanced Linear Autoencoders for Recommendation
Jaewan Moon, Seongmin Park, Jongwuk Lee
Large language models (LLMs) have been widely adopted to enrich the semantic representation of textual item information in recommender systems. However, existing linear autoencoder…
MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation
Ilwoong Baek, Mincheol Yoon, Seongmin Park +1
Sequential recommendation (SR) aims to predict users' subsequent interactions by modeling their sequential behaviors. Recent studies have explored frequency domain analysis, which…