Tags: Conversational Recommender Systems - Paper Library

Stop Playing the Guessing Game! Target-free User Simulation for Evaluating Conversational Recommender Systems

Published:11/25/2024

Conversational Recommender SystemsUser Simulator EvaluationPreference Elicitation Capability AssessmentTarget-Free User SimulationPEPPER Evaluation Protocol

This study introduces PEPPER, a novel evaluation protocol for Conversational Recommender Systems using targetfree user simulators, enhancing assessment realism and helping users discover preferences, overcoming limitations of targetbiased simulators. Experiments validate PEPPER

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LLM-REDIAL: A Large-Scale Dataset for Conversational Recommender Systems Created from User Behaviors with LLMs

Published:8/1/2024

Conversational Recommender SystemsLarge-Scale Conversational Recommendation DatasetIntegration of User Behavior Data and Dialogue TemplatesMulti-Domain Conversational RecommendationLLM-Generated Dialogues

LLMREDIAL is a largescale dataset for conversational recommender systems, addressing limitations of existing datasets. It combines historical user behavior and dialogue templates generated by LLMs, featuring 47.6k multiturn dialogues with consistent semantics, validated by hum

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CRS-Que: A User-centric Evaluation Framework for Conversational Recommender Systems

Published:11/2/2023

Conversational Recommender SystemsUser-Centric Evaluation FrameworkUser Experience Evaluation MetricsMusic Exploration RecommendationMobile Phone Purchase Recommendation

This paper presents CRSQue, a usercentric evaluation framework for conversational recommender systems, built on ResQue. It integrates conversationrelated UX metrics and validates its effectiveness and reliability across different scenarios, highlighting the interaction between

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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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Papers