Recommender systems are vital to online platforms, with Graph Neural Network (GNN) approaches showing excellent accuracy. However, beyond-accuracy metrics like diversity, serendipity, and fairness significantly impact user satisfaction. This review examines these dimensions in GNN-based recommenders, analyzing recent developments that balance accuracy with these broader objectives. We explore how various model development stages—from data preprocessing to training methodologies—affect these metrics. The paper addresses practical challenges in maintaining high accuracy while enhancing diversity, serendipity, and fairness, and outlines future research directions for more holistic GNN-based recommender systems. Our contribution lies in providing a comprehensive understanding of the multidimensional considerations essential to effective recommender system design.
Tomislav Đuričić,
Dominik Kowald,
Emanuel Lacić,
Elisabeth Lex
