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Benchmarking fairness-aware recommender systems from matrix factorization to graph-based models
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AI
DOI：10.1007/s10844-026-01035-w.png)
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Abstract
En 中文
As fairness becomes an increasingly critical concern in recommender systems, evaluating models that balance accuracy and fairness remains a significant research challenge. This paper presents a comprehensive benchmarking study of fairness-aware recommender systems, ranging from classical matrix factorization techniques to state-of-the-art graph-based and neural models. Using the MovieLens 100 K and 1 M datasets, we evaluate eight models-MF, FairMF, FairRec, BiasExplain, NeuMF, MACR, LightGCN, and CERec-across accuracy metrics (AUC, F1, Precision, Recall, Test Loss) and fairness metrics (Statistical Parity, Group AUC, Equal Opportunity, Equalized Odds). Experimental results indicate that NeuMF achieves the highest AUC (0.8359 +/- 0.0101), with CERec (0.8330 +/- 0.1493) and LightGCN (0.8320 +/- 0.0042) close behind; CERec and LightGCN offer competitive trade-offs between accuracy and fairness. Notably, LightGCN exhibits the most stable performance across seeds, whereas CERec provides a strong performance-fairness trade-off despite its higher variance. Among fairness-aware models, FairRec achieves the lowest Statistical Parity (0.0077 +/- 0.0066), while BiasExplain and FairMF yield marginal fairness gains at the cost of accuracy. Visualization analyses (ROC, PR curves, PCA, and t-SNE) further reveal representational distinctions and clustering behaviors among models. Our findings highlight that high-performing models can still uphold fairness, and traditional fairness-aware approaches may require refinement. We conclude with recommendations for model deployment and future research in fairness-driven recommendation.
Keywords:
Benchmark evaluation
Recommender systems
Matrix factorization
Graph neural networks
Fairness-aware recommendation
Causal fairness
Performance-fairness trade-off
Journal
J
IF:
3.4
Papers:
75
Citations:
0