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 categorize relevant publications into four groups: dataset construction, algorithm design, system evaluation, and empirical studies, providing a comprehensive analysis of simulation methods in CRSs research. Our analysis reveals that simulation methods play a key role in tackling CRSs' main challenges. For example, LLM-based simulation methods have been used to create conversational recommendation data, enhance CRSs algorithms, and evaluate CRSs. Despite several challenges, such as dataset bias, the limited output flexibility of LLM-based simulations, and the gap between text semantic space and behavioral semantics, persist due to the complexity in Human-Computer Interaction (HCI) of CRSs, simulation methods hold significant potential for advancing CRS research. This review offers a thorough summary of the current research landscape in this domain and identifies promising directions for future inquiry.
Code (0)
등록된 구현이 없습니다.
Tasks
Conversational RecommendationRecommendation SystemsSimilar Papers 제목 키워드 기반
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review
Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches int…
Model SelectionRecommendation SystemsSystematic Literature ReviewTidying Up the Conversational Recommender Systems' Biases
The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research circles. However, concerns regarding biases in these systems have emerged. Whil…
Natural Language UnderstandingRecommendation SystemsIdentifying Breakdowns in Conversational Recommender Systems using User Simulation
We present a methodology to systematically test conversational recommender systems with regards to conversational breakdowns. It involves examining conversations generated between the system and simulated users for a set…
Conversational RecommendationDiagnosticUser SimulationMulti-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
Conversational recommender systems enable natural language conversations and thus lead to a more engaging and effective recommendation scenario. As the conversations for recommender systems usually contain limited contex…
Mixture-of-ExpertsRecommendation SystemsEvaluating Conversational Recommender Systems: A Landscape of Research
Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural la…
Decision MakingRecommendation Systems