# Muse: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles

arXiv: https://arxiv.org/abs/2412.18416 · HTML: https://mlsanity.com/paper/2412.18416/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2024.

## Built on by (most cited first, 8 of 8)

- [LettinGo: Explore User Profile Generation for Recommendation System](https://mlsanity.com/paper/2506.18309/) (2025)
- [Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation](https://mlsanity.com/paper/2604.09698/) (2026)
- [MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation](https://mlsanity.com/paper/2510.05124/) (2025)
- [HARPO: Hierarchical Agentic Reasoning for User-Aligned Conversational Recommendation](https://mlsanity.com/paper/2604.10048/) (2026)
- [Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations](https://mlsanity.com/paper/2508.18132/) (2025)
- [Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation](https://mlsanity.com/paper/2604.20749/) (2026)
- [Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants](https://mlsanity.com/paper/2606.24194/) (2026)
- [Re2A: Situated Conversational Recommendation via Rubric-based Preference Reasoning and Alignment](https://mlsanity.com/paper/2609.18249/) (2026)

## Abstract (arXiv)

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.