Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective

To date, graph collaborative filtering (CF) strategies have outperformed pure CF models in generating accurate recommendations. However, concerns about fairness and potential biases in recommendations have emerged, as unfair recommendations may harm the interests of Consumers and Producers (CP). Recognizing the lack of a thorough evaluation of graph CF on CP-aware fairness measures, we initially assessed the effects of eight state-of-the-art graph models and four pure CF recommenders on CPaware fairness measures. Surprisingly, graph CF solutions do not ensure significant item exposure and user fairness. To unravel this performance puzzle, we propose a taxonomy for graph CF, highlighting differences in node representation and neighborhood exploration. Through this lens, the experimental outcomes become clear and pave the way for a multi-objective CP-fairness analysis (Codes are available at: https://github.com/sisinflab/ECIR2023-Graph-CF).