# Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

arXiv: https://arxiv.org/abs/1606.02096 · HTML: https://mlsanity.com/paper/1606.02096/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [Automatic Music Playlist Generation via Simulation-based Reinforcement Learning](https://mlsanity.com/paper/2310.09123/) (2023)
- [Pruning-Based Extraction of Descriptions from Probabilistic Circuits](https://mlsanity.com/paper/2311.13379/) (2023)
- [BASS: Benchmarking Audio LMs for Musical Structure and Semantic Reasoning](https://mlsanity.com/paper/2602.04085/) (2026)

## Abstract (arXiv)

We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists.