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cs.IR2025
PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Ruining He, Lukasz Heldt, Lichan Hong +20
Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit signi…
cs.IR2025
ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
Yupeng Hou, Jianmo Ni, Zhankui He +5
Generative recommendation (GR) is an emerging paradigm where user actions are tokenized into discrete token patterns and autoregressively generated as predictions. However, existin…
cs.IR2024
STAR: A Simple Training-free Approach for Recommendations using Large Language Models
Dong-Ho Lee, Adam Kraft, Long Jin +5
Recent progress in large language models (LLMs) offers promising new approaches for recommendation system tasks. While the current state-of-the-art methods rely on fine-tuning LLMs…