mlsanityData to 7 Oct 2026

Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models

arXiv 2504.12313 ↗First posted 9 Apr 2025cs.CL cs.HCcited by 4

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Abstract (arXiv)

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 natural and dynamic user interactions. However, a key challenge remains in understanding how personality traits shape conversational recommendation outcomes. Psychological evidence highlights the influence of personality traits on user interaction behaviors. To address this, we introduce an LLM-based personality-aware user simulation for CRSs (PerCRS). The user agent induces customizable personality traits and preferences, while the system agent possesses the persuasion capability to simulate realistic interaction in CRSs. We incorporate multi-aspect evaluation to ensure robustness and conduct extensive analysis from both user and system perspectives. Experimental results demonstrate that state-of-the-art LLMs can effectively generate diverse user responses aligned with specified personality traits, thereby prompting CRSs to dynamically adjust their recommendation strategies. Our experimental analysis offers empirical insights into the impact of personality traits on the outcomes of conversational recommender systems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 47 references that are in the map. First posted on arXiv 9 Apr 2025.

Builds on

16 of 47 references.

References · 16

  1. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 229
  2. 2023InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviewscited by 177
  3. 2020A Survey on Conversational Recommender Systemscited by 142
  4. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  5. 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
  6. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  7. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 112
  8. 2022Who is GPT-3? An Exploration of Personality, Values and Demographicscited by 112
  9. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  10. 2020Interactive Path Reasoning on Graph for Conversational Recommendationcited by 72
  11. 2021DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendationcited by 46
  12. 2021Learning to Ask Appropriate Questions in Conversational Recommendationcited by 35
4 more
  1. 2024Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM argumentscited by 30
  2. 2023In-Context Learning User Simulators for Task-Oriented Dialog Systemscited by 19
  3. 2023Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialoguecited by 15
  4. 2023UPLex: Fine-Grained Personality Control in Large Language Models via Unsupervised Lexical Modulationcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025RecoWorld: Building Simulated Environments for Agentic Recommender Systemscited by 18
  2. 2025IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendationcited by 5
  3. 2025Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Expertscited by 4
  4. 2026Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversationcited by 3