Papers Conversational Recommendation
“Conversational Recommendation” 태그가 달린 논문 140편 · 필터 해제
A Literature Review on Simulation in Conversational Recommender Systems
Conversational Recommender Systems (CRSs) have garnered attention as a novel approach to delivering personalized recommendations through multi-turn dialogues. This review developed a taxonomy framework to systematically …
Conversational RecommendationRecommendation SystemsExpectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs). However, these agents often generate short-sighted responses that fail to s…
Conversational RecommendationConversational Recommendation System using NLP and Sentiment Analysis
In today's digitally-driven world, the demand for personalized and context-aware recommendations has never been greater. Traditional recommender systems have made significant strides in this direction, but they often lac…
Conversational RecommendationDynamic Time WarpingMarketingRecommendation Systems+2Minimally Supervised Hierarchical Domain Intent Learning for CRS
Modeling domain intent within an evolving domain structure presents a significant challenge for domain-specific conversational recommendation systems (CRS). The conventional approach involves training an intent model usi…
ClusteringConversational RecommendationRecommendation SystemsSearch-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. A fundamental challenge in CRSs lies in effectively understanding user prefere…
AttributeConversational RecommendationRecommendation SystemsMATCHA: Can Multi-Agent Collaboration Build a Trustworthy Conversational Recommender?
In this paper, we propose a multi-agent collaboration framework called MATCHA for conversational recommendation system, leveraging large language models (LLMs) to enhance personalization and user engagement. Users can re…
Conversational RecommendationDiversityRecommendation SystemsRe-RankingBeyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation
Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typically model the dialogue context as a wh…
Conversational RecommendationcounterfactualCounterfactual InferenceDisentanglement+3HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models
Seeking dietary guidance often requires navigating complex professional knowledge while accommodating individual health conditions. Knowledge Graphs (KGs) offer structured and interpretable nutritional information, where…
Conversational RecommendationExplainable RecommendationKnowledge GraphsExploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Conversational Recommender Systems (CRSs) engage users in multi-turn interactions to deliver personalized recommendations. The emergence of large language models (LLMs) further enhances these systems by enabling more nat…
Conversational RecommendationRecommendation SystemsUser SimulationLaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation
Conversational recommender systems engage users in dialogues to refine their needs and provide more personalized suggestions. Although textual information suffices for many domains, visually driven categories such as fas…
Conversational RecommendationRecommendation SystemsMemInsight: Autonomous Memory Augmentation for LLM Agents
Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these …
Conversational RecommendationLanguage ModelingLanguage ModellingLarge Language Model+4Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences
Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that capturing sentiments in user conversations …
Conversational RecommendationRecommendation SystemsRetrievalREGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives
This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Models (LLMs), addressing the limitations of …
Conversational RecommendationHarmonizing Large Language Models with Collaborative Behavioral Signals for Conversational Recommendation
Conversational recommendation frameworks have gained prominence as a dynamic paradigm for delivering personalized suggestions via interactive dialogues. The incorporation of advanced language understanding techniques has…
Collaborative FilteringConversational RecommendationGraph Retrieval-Augmented LLM for Conversational Recommendation Systems
Conversational Recommender Systems (CRSs) have emerged as a transformative paradigm for offering personalized recommendations through natural language dialogue. However, they face challenges with knowledge sparsity, as u…
Conversational RecommendationIn-Context LearningLarge Language ModelNatural Language Understanding+3Federated Conversational Recommender System
Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users in a conversational manner to learn the…
Conversational RecommendationRecommendation SystemsTALKPLAY: Multimodal Music Recommendation with Large Language Models
We present TALKPLAY, a novel multimodal music recommendation system that reformulates recommendation as a token generation problem using large language models (LLMs). By leveraging the instruction-following and natural l…
Conversational RecommendationInstruction FollowingLanguage ModelingLanguage Modelling+5Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their superior understanding of context-aware…
Collaborative FilteringConversational RecommendationLanguage ModelingLanguage Modelling+4STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counseling
Online psychological counseling dialogue systems are trending, offering a convenient and accessible alternative to traditional in-person therapy. However, existing psychological counseling dialogue systems mainly focus o…
Conversational RecommendationDialogue GenerationQuestion AnsweringBridging Conversational and Collaborative Signals for Conversational Recommendation
Conversational recommendation systems (CRS) leverage contextual information from conversations to generate recommendations but often struggle due to a lack of collaborative filtering (CF) signals, which capture user-item…
Collaborative FilteringConversational RecommendationRecommendation Systems