Balancing Accuracy Robustness and Explainability in E-Commerce Recommender Systems

Recommender systems are essential to digital marketplaces, shaping how users discover products and engage with platforms. While AI has significantly improved accuracy, critical concerns about robustness and explainability remain. This study introduces and empirically validates the “Recommender’s Trilemma”—an inherent trade-off between accuracy, robustness, and explainability. Through comparative analysis of NeuMF, SVD, and TF-IDF on the Amazon Electronics dataset, we uncover a dual failure cascade: adversarial attacks not only degrade recommendation quality but also destabilize the explanations meant to foster user trust. While NeuMF achieves high accuracy, it is susceptible to data poisoning that undermines its decision logic; in contrast, the transparent TF-IDF model offers interpretability but suffers from low predictive power and brittle explanations. These findings expose a structural vulnerability in recommender system design and provide a diagnostic framework for auditing deployed systems. We call for a new development paradigm where robustness and explainability are treated as co-primary objectives alongside accuracy—enabling trustworthy, resilient, and ethically aligned AI in digital commerce.

@article{Alohali2026,
  title     = {Balancing Accuracy Robustness and Explainability in E-Commerce Recommender Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mansor Alohali},
  doi       = {10.14569/IJACSA.2026.0170408},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170408}
}