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    <abstract>Current conversational recommendation systems focus predominantly on text. However, real-world recommendation settings are generally multimodal, causing a significant gap between existing research and practical applications. To address this issue, we propose Muse, the first multimodal conversational recommendation dataset. Muse comprises 83,148 utterances from 7,000 conversations centered around the Clothing domain. Each conversation contains comprehensive multimodal interactions, rich elements, and natural dialogues. Data in Muse are automatically synthesized by a multi-agent framework powered by multimodal large language models (MLLMs). It innovatively derives user profiles from real-world scenarios rather than depending on manual design and history data for better scalability, and then it fulfills conversation simulation and optimization. Both human and LLM evaluations demonstrate the high quality of conversations in Muse. Additionally, fine-tuning experiments on three MLLMs demonstrate Muse’s learnable patterns for recommendations and responses, confirming its value for multimodal conversational recommendation. Our dataset and codes are available at https://anonymous.4open.science/r/Muse-0086.</abstract>
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%0 Conference Proceedings
%T MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles
%A Wang, Zihan
%A Yang, Xiaocui
%A Liu, YongKang
%A Feng, Shi
%A Wang, Daling
%A Zhang, Yifei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-muse
%X Current conversational recommendation systems focus predominantly on text. However, real-world recommendation settings are generally multimodal, causing a significant gap between existing research and practical applications. To address this issue, we propose Muse, the first multimodal conversational recommendation dataset. Muse comprises 83,148 utterances from 7,000 conversations centered around the Clothing domain. Each conversation contains comprehensive multimodal interactions, rich elements, and natural dialogues. Data in Muse are automatically synthesized by a multi-agent framework powered by multimodal large language models (MLLMs). It innovatively derives user profiles from real-world scenarios rather than depending on manual design and history data for better scalability, and then it fulfills conversation simulation and optimization. Both human and LLM evaluations demonstrate the high quality of conversations in Muse. Additionally, fine-tuning experiments on three MLLMs demonstrate Muse’s learnable patterns for recommendations and responses, confirming its value for multimodal conversational recommendation. Our dataset and codes are available at https://anonymous.4open.science/r/Muse-0086.
%R 10.18653/v1/2025.findings-acl.58
%U https://aclanthology.org/2025.findings-acl.58/
%U https://doi.org/10.18653/v1/2025.findings-acl.58
%P 1027-1053

Markdown (Informal)

[MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles](https://aclanthology.org/2025.findings-acl.58/) (Wang et al., Findings 2025)

• MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles (Wang et al., Findings 2025)

ACL

• Zihan Wang, Xiaocui Yang, YongKang Liu, Shi Feng, Daling Wang, and Yifei Zhang. 2025. MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles. In Findings of the Association for Computational Linguistics: ACL 2025, pages 1027–1053, Vienna, Austria. Association for Computational Linguistics.