7 papers
Topology-Aware Tokenization for Generative Recommendation
Yaokun Liu, Yifan Liu, Zhenrui Yue +4
Generative recommendation reformulates sequential recommendation as an autoregressive generation task, yet a critical issue in this paradigm remains overlooked: topology distortion…
Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
Yifan Liu, Yaokun Liu, Zelin Li +5
Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenizer, and then large language models (LLM…
SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
Gyuseok Lee, Wonbin Kweon, Zhenrui Yue +5
Large language models (LLMs) have enhanced conventional recommendation models via user profiling, which generates representative textual profiles from users' historical interaction…
Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization
Gyuseok Lee, Wonbin Kweon, Zhenrui Yue +3
Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, existing methods estimate user wei…
Harmonic Dataset Distillation for Time Series Forecasting
Seungha Hong, Sanghwan Jang, Wonbin Kweon +3
Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD),…
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…