Important Dates

• Initial submission (abstract) : July 14th

• Final submission: July 17th

• Author notification: August 16th

• Camera-ready version: August 31st

• Workshop: September 28th

(every deadline is 23:59, AoE)

Call for Papers

Music recommendation has been a longstanding topic of interest within the RecSys community, reflecting its critical role in shaping listening experiences on music streaming platforms. Despite significant advances, music recommender systems continue to face complex challenges, particularly with the recent surge of generative AI technologies.

This fourth edition of the Music Recommender Systems Workshop (MuRS) focuses on the implications of generative content for recommendation: how it affects discoverability, listener engagement, platform transparency, and the curation of authentic musical experiences. Addressing these issues requires insights from multiple research communities, including RecSys and Music Information Retrieval (MIR).

We invite researchers, from both, various academic disciplines and industry, to submit papers on music recommendation topics that address but are not limited to:

Foundations and Core Methods

• Advances in online and offline evaluation methods for music recommendation

• Bandits and reinforcement learning for music recommendation

• Engineering and systems aspects of large-scale music recommendation

• Large language models for music recommendation

• Multimodal and cross-modal approaches to music recommendation

• Multi-stakeholder and multi-objective optimization in music recommender systems

• Music representation learning and similarity metric learning for recommendation

• Music content understanding and automatic tagging for recommendation

• Sequential music recommendation

Applications and Use Cases

• Conversational music recommender systems

• Cross-domain (including music) recommendation

• Cold-start and popularity-bias mitigation

• Homepage personalization

• Music search, browsing, and discovery

• Music recommendation on social media platforms

• Playlist generation and continuation

• Recommender systems for the live music industry and record labels

• Recommender systems for music creation, co-creation, and generative workflows

• Virtual and augmented reality listening experiences

• User Modelling, Experience, and Interaction

• Human-centric evaluation of music recommender systems

• Listener taste modelling

• Listener intent modelling (session-level and long-term) and context understanding

• User studies on music consumption and interaction patterns

• Psychological aspects in user modeling and recommendation

• Personas, emotion, and mood modeling

Societal, Ethical, and Cultural Dimensions

• Cross-cultural music recommendation

• Empirical studies on the societal impact of algorithmic music recommendation

• Ethical considerations in music recommender systems

• Fairness, transparency, interpretability, and explainability in music recommendation

• Local and regional music recommendation

• Socially-aware music recommender systems

• AI-generated music in the recommendation ecosystem

Submissions and Template

The workshop welcomes the following types of paper contributions:

• Regular papers (max. 8 pages, excl. references) which may include work in progress and preliminary results. 

• Short/Position papers (max. 4 pages, excl. references) describing original ideas, perspectives, research visions, and open challenges.

All submissions should be in English, should not have been accepted for publication for other RecSys workshops, nor be currently under review. We accept previously published works including pre-prints, e.g., on arXiv. Authors of previously accepted works must clearly indicate the format and venue of the previous publication.

The contributions are to be submitted via the joint workshop submission portal on EasyChair. Make sure to select the “4th Music Recommender Workshop” track when creating a submission. For the submissions, authors should use the CEURART single-column template available on CEUR and on Overleaf.

Each submission will be reviewed by three members of the Program Committee through a single-anonymized peer review process (author names and affiliations must be included in the manuscript; reviewer identities will be hidden).

RecSys 2026 is an in-person conference. All papers accepted to MuRS are expected to be presented in-person. At least one author from each accepted paper must register for and attend the workshop in person to present the paper and address audience questions during the Q&A session.