Recommender systems personalize content by predicting user preferences, but this often results in unequal treatment of users and items—for example, some users may receive lower-quality recommendations, while niche items remain underexposed. Although fairness-enhancing interventions exist, they can obscure the extent to which disparities stem from model architecture alone.
This study investigates how collaborative filtering architectures affect both accuracy and fairness. We evaluate six models, including two non-personalized baselines, across two public datasets using a unified pipeline without fairness-specific interventions.
Our results reveal a general trade-off: models with higher accuracy often exhibit greater fairness disparities, particularly on the user side. For example, LightGCN combines strong accuracy with relatively high item-side fairness, while SLIMElastic ranks high in accuracy but worsens unfairness. However, this trade-off is not uniform across datasets; NeuMF degrades notably on sparser data.
These findings demonstrate that model architecture alone can shape fairness–accuracy trade-offs, highlighting the importance of considering dataset characteristics and model design when selecting or developing recommender systems.

This study investigates fairness in knowledge-aware recommender systems by evaluating their performance across both accuracy and fairness metrics. Using the MovieLens 1M dataset, we compare general, knowledge-aware, and fairness-optimized models through a custom RecBole-based pipeline. Results indicate knowledge-aware models offer some fairness benefits without major accuracy loss, though no model excels universally. Adjusting loss component weights reveals complex trade-offs and component importance, underscoring the need for nuanced fairness optimization.