Papers
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Conversational Recommender Systems
USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model
Published:9/21/2025
RL Training for Large Language ModelsConversational Recommender SystemsLLM-based Recommendation SystemsUser-Simulator-Based FrameworkPreference Optimization Dataset Construction
The USBRec framework enhances Large Language Models' capabilities in conversational recommendation through a usersimulatorbased preference optimization dataset and a selfenhancement strategy. Extensive experiments show it consistently outperforms existing stateoftheart met
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Refining Text Generation for Realistic Conversational Recommendation via Direct Preference Optimization
Published:8/27/2025
Conversational Recommender SystemsDirect Preference OptimizationLLM Applications in RecommendationUser Preference ExtractionText Generation Refinement
This paper introduces an improved Conversational Recommender System method using Large Language Models to generate dialogue summaries and recommendations, capturing both explicit and implicit user preferences. Direct Preference Optimization (DPO) is employed to ensure rich conten
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Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs
Published:9/8/2025
Conversational Recommender SystemsLLM-based Recommendation SystemsUser Interest Broadness ModelingEntropy-Driven Dialogue PolicyDynamic Exploration and Exploitation in Recommendations
This study introduces a conversational recommender system strategy that balances exploration and exploitation by modeling user interest breadth through retrieval score entropy, dynamically adjusting dialogue policies based on query specificity to enhance user experience.
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Beyond Persuasion: Towards Conversational Recommender System with
Credible Explanations
Published:9/22/2024
LLM-based Recommendation SystemsGenerative Recommendation SystemsCredible Explanation GenerationConversational Recommender Systems
This paper proposes PCCRS, enhancing conversational recommenders with credibilityaware strategies and posthoc selfreflection, improving both explanation trustworthiness and recommendation accuracy.
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CARE: Contextual Adaptation of Recommenders for LLM-based Conversational
Recommendation
Published:8/19/2025
LLM-based Recommendation SystemsConversational Recommender SystemsContext-Aware RecommendationEntity-Level Recommendation EnhancementRecommendation Reranking
CARE framework integrates external recommenders with LLMs, enabling domain adaptation and leveraging context and collaborative relationships to improve accuracy and diversity in conversational recommendations.
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