Multimodal Music Recommendation System using LLMs
Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has explored LLM-augmented, multimodal, and text-enhanced approaches to sequential recommendation, and while some methods partially combine semantic, acoustic, or engagement signals, none jointly model all three within a unified LLM-based sequential reasoning framework that grounds recommendations in actual song content. In this work, we propose a multimodal framework for session-based music recommendation that enriches the LastFM-1K dataset with three complementary signals: (1) audio and lyric embeddings extracted usi...
| Authors | Srikar Prabhas Kandagatla, Sreehitha R. Narayana, Chandana Magapu et al. |
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| Primary category | cs.IR |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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