DEVELOPMENT OF A REGULARIZATION-BASED FAIRNESS-AWARE LOSS FUNCTION FOR MITIGATING POPULARITY BIAS IN MOVIE RECOMMENDER SYSTEMS

Abstract

Recommender systems are a cornerstone of the movie entertainment industry, driving user engagement and personalizing content delivery to enhance customer experience. However, popularity bias where certain content is disproportionately recommended can limit the visibility of diverse contents, undermining innovation and customer satisfaction. This study proposed a regularization-based fairness-aware mechanism designed to mitigate popularity bias in recommender systems. The proposed mechanism integrates fairness-aware loss functions, such as exposure fairness loss, fairness-aware ranking loss and disparate Impact Loss, into the Neural Collaborative Filtering recommendation algorithm. The fairness-aware model was integrated into a movie recommendation system. The system was evaluated for technical effectiveness in terms of recommendation accuracy, fairness in exposure, and usability in real-world entertainment business contexts. All models achieved very high exposure fairness (≥0.9995). REBFAL matched the best baselines in long-tail coverage (0.2987) while showing a slightly higher exposure imbalance (0.7487), reflecting a trade-off between fairness and distribution.