Supervisor
Dr. Taufique Ahmed
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Music recommender systems play an important role in helping users navigate large catalogues, but their reliance on historical interaction data can reinforce popularity bias and limit the visibility of less popular, long-tail items. This research develops and evaluates a bias-aware framework for assessing and mitigating popularity bias in music recommendation using implicit user-item interaction data. The study first quantifies popularity bias through inequality and long-tail metrics, before evaluating two baseline recommender approaches: a non-personalised Most-Popular model and a personalised Matrix Factorisation model. Their performance is assessed using both predictive accuracy measures, including Precision, Recall and NDCG, and beyond-accuracy measures relating to exposure concentration, catalogue diversity and long-tail discovery. A popularity-aware post-processing re-ranking strategy is then implemented and evaluated under an explicit constraint on accuracy loss. The comparative analysis examines whether re-ranking can improve long-tail exposure and reduce popularity concentration while maintaining acceptable recommendation accuracy. Where appropriate, paired user-level statistical tests are used to assess differences between approaches. The research provides an evaluation framework for examining the trade-off between recommendation accuracy and discovery-oriented outcomes in music recommender systems.
Date of Award
2026
Full Publication Date
2026
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Maidana, J.
(2026) Balancing Accuracy and Discovery: A Bias-Aware Evaluation Framework for Music Recommendation CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.157