{"type":"ScholarlyArticle","title":"Do Mentioned Items Truly Matter? Enhancing Conversational Recommender Systems with Causal Intervention and Large Language Models","authors":[{"name":"Lingzhi Wang","slug":"lingzhi-wang","given":"Lingzhi","family":"Wang","affiliation":""},{"name":"Xingshan Zeng","slug":"xingshan-zeng","given":"Xingshan","family":"Zeng","affiliation":""},{"name":"Kam-Fai Wong","slug":"kam-fai-wong","given":"Kam-Fai","family":"Wong","affiliation":""}],"abstract":"Conversational Recommender Systems (CRS) have become increasingly important due to their ability to recommend items through interactive dialogue, adapting to user preferences in real time. Traditional CRS approaches face challenges in generating high-quality, diverse responses due to the limited availability of training data and the inherited biases from domain-specific fine-tuning. Furthermore, existing systems often overlook the impact of confounding variables during user interactions, leading to suboptimal recommendations. In this work, we propose a novel hybrid framework that integrates large language models (LLMs) with traditional recommendation techniques to address these limitations. Our approach leverages the strengths of LLMs in generating fluent, contextually appropriate responses while employing a traditional recommendation module to capture complex interaction structures. To ensure unbiased recommendations, we introduce causal interventions that disentangle confounding variables, improving recommendation accuracy. We evaluate our framework on established CRS datasets, demonstrating significant improvements in recommendation quality and response generation. Our results highlight the effectiveness of the causal intervention mechanism in producing more reliable and personalized recommendations, while the LLM-based response generation offers scalability across multiple domains.","doi":"10.24963/ijcai.2025/470","url":"https://ijcai.org/proceedings/2025/470","pdf":"https://ijcai.org/proceedings/2025/0470.pdf","markdown":"https://ijcai.org/proceedings/2025/470.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/470","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Humans and AI","track":"Main Track","number":470,"pages":{"first":4218,"last":4226},"keywords":["Humans and AI: HAI: Personalization and user modeling","Humans and AI: HAI: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}