TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
arXiv:2509.09685v5 Announce Type: replace-cross Abstract: We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline. In the proposed pipeline, multiple large language model (LLM) agents are created under various roles with specialized prompts and access to different parts of information, and the chat data is acquired by logging the conversation between the Listener LLM and the Recsys LLM. To cover various conversation scenarios, for each conversation, the Listener LLM is conditioned on a finetuned conversation goal.
The proliferation of advanced LLMs enables more sophisticated agentic pipelines for synthetic data generation, addressing the increasing demand for high-quality, diverse training data for conversational AI.
This development allows for the creation of rich, domain-specific datasets at scale, reducing reliance on expensive and privacy-sensitive real-world data collection for niche applications like music recommendation.
The ability to generate multimodal conversational data synthetically using LLM agents changes how training data is acquired and refined for AI systems, particularly in interactive and personalized recommendation engines.
- · AI model developers
- · Music streaming platforms
- · Generative AI companies
- · User experience designers
- · Traditional data collection firms
- · Manual data annotation services
The availability of 'TalkPlayData 2' will accelerate development in conversational music recommendation systems.
Improved recommendation systems could lead to more personalized user experiences and increased engagement on music platforms.
Enhanced AI-driven personalization might further entrench dominant platforms, making competition harder for new entrants.
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Read at arXiv cs.AI