Personalized course-recommendation systems can help students make better academic choices and improve learning outcomes. Matrix factorization (MF) is a well-established and effective approach for this task, producing accurate recommendations from historical student–course performance data. However, the deployment of MF-based recommenders is hindered by privacy and regulatory risks, particularly when sensitive student records are processed by third-party or centralized systems. In the privacy-preserving setting, MF models exhibit reduced accuracy: when combined with differential privacy, accuracy is fundamentally degraded by the added noise, while existing cryptography-based approaches omit bias terms, resulting in a measurable accuracy gap with their plaintext equivalents.
This thesis enhances a Homomorphic-Encryption-based recommendation protocol to support biased Matrix Factorization through two additions: data centering and vector augmentation. These modifications maintain the security guarantees of the original protocol under the semi-honest adversary model while enabling the model to incorporate user and item biases. Evaluated in the plaintext domain on the MovieLens-100k dataset, the enhanced model achieved a test RMSE of 0.9213, a notable improvement over the baseline's 0.9507, and reached the baseline’s best RMSE with only 15 training iterations instead of 145. Beyond accuracy and efficiency, separating bias terms from the student–course interaction extends the system from a simple grade predictor into a tool for academic discovery, allowing for recommendations that consider inherent compatibility, not solely predicted grades. Although demonstrated in a course-recommendation setting, the approach is applicable to any privacy-preserving recommender system, offering reduced computational costs and narrowing the accuracy gap with non-private methods.