Papers
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LLM-based Recommendation Systems
TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation
Published:6/15/2024
LLM-based Recommendation SystemsGenerative Recommendation SystemsUser-Item ID TokenizationMasked Vector-Quantized TokenizerCapturing High-Order Collaborative Knowledge for LLMs
TokenRec is introduced as a novel framework for enhancing LLMbased recommendation systems by effectively tokenizing user and item IDs. Featuring the Masked VectorQuantized Tokenizer and generative retrieval, it captures highorder collaborative knowledge, improving recommendati
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LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation
Published:11/18/2025
LLM-based Recommendation SystemsGeographic Item TokenizationLocal-Life RecommendationReinforcement Learning Geographic AlignmentHierarchical Geographic Item Tokenization
The LGSID framework enhances locallife recommendation by integrating RLbased geographic alignment and hierarchical item tokenization to capture spatial relationships, outperforming existing models in empirical studies.
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ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Published:6/27/2025
Multi-Agent Personalized Recommendation SystemAugmented Retrieval-Generation FrameworkLLM-based Recommendation SystemsDynamic User Preference ModelingRecommendation System Evaluation
The ARAG framework enhances personalized recommendations by integrating a multiagent collaboration into RetrievalAugmented Generation. It employs agents for user understanding, natural language inference, context summarization, and item ranking, outperforming traditional RAG me
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SCoTER: Structured Chain-of-Thought Transfer for Enhanced Recommendation
Published:11/24/2025
LLM-based Recommendation SystemsStructured Chain-of-Thought TransferPattern Discovery MechanismEfficient Model Structure IntegrationOnline Recommendation System Optimization
SCoTER is a framework designed to enhance recommendation systems by efficiently integrating Large Language Models' reasoning capabilities. It addresses key challenges through automated pattern discovery and structurepreserving integration, demonstrating improved performance thro
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