Recommender systems often struggle to balance global preference patterns, local similarity, and complex user-item interactions within a single model. Existing approaches combine traditional collaborative filtering (CF) methods such as Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) with gradient boosting models such as XGBoost as meta-learners, yet they often fail to capture complex non-linear interactions. In addition, XGBoost may exhibit limitations in stability, as it can become sensitive to training data and show signs of overfitting in certain scenarios. To address this limitation, this study proposes a stacking ensemble-based CF approach that extends a baseline model by incorporating Neural Collaborative Filtering (NCF) as an additional base learner and employing Random Forest (RF) as the meta-learner. These base models capture complementary characteristics: SVD models global latent factors, KNN captures local similarity, and NCF learns non-linear user-item interactions. Experiments are conducted on the MovieLens 100K dataset. The results demonstrate that augmenting the baseline model with NCF and RF reduces prediction ambiguity and improves system convergence stability. The proposed ensemble achieves the lowest RMSE of 0.9113. Although the baseline model with XGBoost slightly outperforms in MAE, the proposed approach achieves higher Recall and F1-score. These results indicate better overall recommendation quality.
Muhammad Faried Gunawan, Rita Rismala· International Conference on...· 0 citations
Deep recommender systems frequently suffer from algorithmic biases that lead to unequal recommendation exposure across demographic groups, particularly disadvantaging underrepresented users. Although various debiasing methods have been proposed, they typically rely on invasive architectural adjustments that disrupt the core algorithm, causing a severe trade-off between fairness and predictive accuracy. To address this, we introduce a novel comparative framework that leverages Generative Adversarial Network (GAN)-based data augmentation to fix the root cause of data imbalance and enhance user-side fairness without sacrificing recommendation performance. Using Neural Collaborative Filtering (NCF) on the MovieLens dataset, we systematically benchmark two distinct augmentation strategies. First, we evaluate decoupled fairness augmentation, which pairs traditional GAN models (CTGAN, GReaT, and TAEGAN) with external preprocessing mitigations. Second, we evaluate intrinsic fairness-aware augmentation, which employs GAN models with embedded fairness mechanisms (TabFairGAN, DECAF, and CFGAN). Performance is evaluated using a comprehensive suite of fairness and recommendation quality metrics. The results demonstrate that GAN-based augmentation significantly improves user-side fairness while preserving overall recommendation performance. Notably, the most effective approach, TabFairGAN, achieves the best trade-off between fairness and accuracy, recording the lowest Uabs of 0.003 and the highest NDCG@10 of 0.811. These findings establish GAN-based data augmentation as an effective, non-invasive strategy for mitigating user-side bias in recommender systems.
Radhofan Azizi Ramdhani, Rita Rismala· International Conference on...· 0 citations
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