# Current Challenges and Visions in Music Recommender Systems Research

arXiv: https://arxiv.org/abs/1710.03208 · HTML: https://mlsanity.com/paper/1710.03208/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

## Built on by (most cited first, 30 of 42)

- [An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention](https://mlsanity.com/paper/2312.10325/) (2023)
- [A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps](https://mlsanity.com/paper/2106.03819/) (2021)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios](https://mlsanity.com/paper/2409.17864/) (2024)
- [Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders](https://mlsanity.com/paper/2108.01053/) (2021)
- [Flow Moods: Recommending Music by Moods on Deezer](https://mlsanity.com/paper/2207.11229/) (2022)
- [A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits](https://mlsanity.com/paper/2110.11819/) (2021)
- [Detecting discriminatory risk through data annotation based on Bayesian inferences](https://mlsanity.com/paper/2101.11358/) (2021)
- [A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services](https://mlsanity.com/paper/2304.09061/) (2023)
- [Hierarchical Latent Relation Modeling for Collaborative Metric Learning](https://mlsanity.com/paper/2108.04655/) (2021)
- [Linear Bandits with Memory: from Rotting to Rising](https://mlsanity.com/paper/2302.08345/) (2023)
- [A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries](https://mlsanity.com/paper/2303.06944/) (2023)
- [Track Mix Generation on Music Streaming Services using Transformers](https://mlsanity.com/paper/2307.03045/) (2023)
- [Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging](https://mlsanity.com/paper/2102.00201/) (2021)
- [On the Consistency of Average Embeddings for Item Recommendation](https://mlsanity.com/paper/2308.12767/) (2023)
- [Let's Get It Started: Fostering the Discoverability of New Releases on Deezer](https://mlsanity.com/paper/2401.02827/) (2024)
- [ReCODE: Modeling Repeat Consumption with Neural ODE](https://mlsanity.com/paper/2405.16550/) (2024)
- [Integrating Item Relevance in Training Loss for Sequential Recommender Systems](https://mlsanity.com/paper/2305.10824/) (2023)
- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling](https://mlsanity.com/paper/2507.00518/) (2025)
- [Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations](https://mlsanity.com/paper/2307.13165/) (2023)
- [Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery](https://mlsanity.com/paper/2308.14601/) (2023)
- [Generalized User Representations for Transfer Learning](https://mlsanity.com/paper/2403.00584/) (2024)
- [Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data](https://mlsanity.com/paper/2408.16430/) (2024)
- [Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics](https://mlsanity.com/paper/2510.16208/) (2025)
- [Psychologically-Inspired Music Recommendation System](https://mlsanity.com/paper/2205.03459/) (2022)
- [The Application of Affective Measures in Text-based Emotion Aware Recommender Systems](https://mlsanity.com/paper/2305.04796/) (2023)
- [Of Spiky SVDs and Music Recommendation](https://mlsanity.com/paper/2307.01212/) (2023)
- [A Computational Analysis of Lyric Similarity Perception](https://mlsanity.com/paper/2404.02342/) (2024)

## Abstract (arXiv)

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.