Publication Date


Document Type

Conference Proceeding

Publication Title

Information Research




Special Issue




Introduction. Music providers like Spotify leverage music recommendation systems to connect users with relevant music. Based on content-based and collaborative-filtering statistical methods, these machine learning algorithms quantify user-song probabilities and present the highest-ranked songs. However, most music providers do not fully address their users’ music seeking and retrieval needs. Likewise, the fields of Recommender Systems, Music Recommendation Systems (MRS) and Music Information Retrieval (MIR) remain disconnected from real-world use cases of music seeking. Method. In this conceptual paper, we review the literature of the Recommender Systems, MRS, MIR and Music Therapy (MT) academic fields. We discuss trends towards greater user control and personalization in the MRS and MIR fields and the connections between MT and positive health outcomes such as reductions in stress, anxiety and heart rate.Analysis. We argue that greater control and visibility into the characteristics of songs and recommended items can generate positive downstream benefits. We recommend features that empower users to better seek, find, store, retrieve and learn from their musical catalogs.Results. We suggest design enhancements that recognize music’s wider psychological and physiological benefits and create opportunities to build domain knowledge. Conclusions. Unlocking music’s myriad benefits through the enhancements proposed would catalyze positive outcomes for business stakeholders, users and society.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.