A Literature Review on Simulation in Conversational Recommender Systems

TL;DR

This review systematically categorizes simulation methods in Conversational Recommender Systems, highlighting their role in addressing key challenges and outlining future research directions.

Contribution

It introduces a taxonomy framework for classifying simulation research in CRSs and analyzes the impact of LLM-based simulation methods.

Findings

Simulation methods are crucial for tackling CRS challenges.

LLM-based simulations improve data creation and system evaluation.

Persistent issues include dataset bias and semantic gap.

Abstract

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.