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Online Recommendation System Optimization
Inductive Generative Recommendation via Retrieval-based Speculation
Published:10/4/2024
Generative Recommendation SystemsTraining-Free Acceleration MethodsOnline Recommendation System OptimizationSequential Recommender SystemsImage Generation
The paper introduces , a retrievalbased inductive generative recommendation framework that addresses the limitations of generative models in recommending unseen items by utilizing a drafter model for candidate generation and a generative model for verification, enhancing
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UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration
Published:10/28/2025
Generative Recommendation SystemsKnowledge Graph-based RecommendationPersonalized Recommendation SystemMultimodal Recommendation SystemsOnline Recommendation System Optimization
The paper introduces UNGER, a generative recommendation approach that integrates semantic and collaborative information into a unified code to reduce storage and inference costs. Utilizing a twophase framework for effective code construction, it demonstrates significant improvem
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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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Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
Published:3/4/2025
Sparse-Dense Recommendation ModelGenerative Recommendation SystemsCascaded Sparse-Dense RepresentationsUser Interaction Sequence ModelingOnline Recommendation System Optimization
The study introduces the COBRA framework, which integrates sparse semantic IDs and dense vectors through alternating generation. This endtoend training enhances dynamic optimization of representations, effectively capturing semantic and collaborative insights from useritem int
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