# A Deep Multimodal Approach for Cold-start Music Recommendation

arXiv: https://arxiv.org/abs/1706.09739 · HTML: https://mlsanity.com/paper/1706.09739/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation](https://mlsanity.com/paper/2508.04571/) (2025)
- [VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation](https://mlsanity.com/paper/2202.10241/) (2022)
- [Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks](https://mlsanity.com/paper/2409.09026/) (2024)
- [Multimodal Music Recommendation System using LLMs](https://mlsanity.com/paper/2606.00125/) (2026)

## Abstract (arXiv)

An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce? In this work we aim to address this so-called cold-start problem by combining text and audio information with user feedback data using deep network architectures. Our method is divided into three steps. First, artist embeddings are learned from biographies by combining semantics, text features, and aggregated usage data. Second, track embeddings are learned from the audio signal and available feedback data. Finally, artist and track embeddings are combined in a multimodal network. Results suggest that both splitting the recommendation problem between feature levels (i.e., artist metadata and audio track), and merging feature embeddings in a multimodal approach improve the accuracy of the recommendations.