TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916997706547200 |
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| author | Choi, Keunwoo Doh, Seungheon Nam, Juhan |
| author_facet | Choi, Keunwoo Doh, Seungheon Nam, Juhan |
| contents | 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. Finally, all the LLMs are multimodal with audio and images, allowing a simulation of multimodal recommendation and conversation. In the LLM-as-a-judge and subjective evaluation experiments, TalkPlayData 2 achieved the proposed goal in various aspects related to training a generative recommendation model for music. TalkPlayData 2 and its generation code are released at https://talkpl.ai/talkplaydata2. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_09685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation Choi, Keunwoo Doh, Seungheon Nam, Juhan Information Retrieval Artificial Intelligence Multimedia Sound Audio and Speech Processing 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. Finally, all the LLMs are multimodal with audio and images, allowing a simulation of multimodal recommendation and conversation. In the LLM-as-a-judge and subjective evaluation experiments, TalkPlayData 2 achieved the proposed goal in various aspects related to training a generative recommendation model for music. TalkPlayData 2 and its generation code are released at https://talkpl.ai/talkplaydata2. |
| title | TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation |
| topic | Information Retrieval Artificial Intelligence Multimedia Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.09685 |