This thesis presents the design, implementation, and evaluation of a scalable, modular recommendation framework for academic article discovery on the Scopus platform. The research addresses limitations in Scopus’s existing “Related documents” module, which produces static, non-personalized suggestions based solely on metadata keyword overlap. To overcome these constraints, the proposed framework introduces a dual-mode retrieval strategy capable of generating both personalized recommendations, informed by historical user interactions, and non-personalized recommendations, based solely on the context of a target article.

The study begins with an extensive exploratory data analysis (EDA) of 2024 Scopus interaction logs, comprising over 31 million user-item events. A novel data transformation pipeline is developed to convert implicit feedback signals, such as downloads, views, and exports, into continuous-valued preference scores that are suitable for collaborative filtering models. This enables the application of state-of-the-art algorithms despite the absence of explicit ratings.

Four recommendation models are implemented and compared: Bayesian Personalized Ranking (BPR), Factored Item Similarity Model (FISM), Light Graph Convolutional Network (LightGCN), and Knowledge Graph Attention Network (KGAT). Model evaluation is performed using both traditional offline ranking metrics (Recall@10, Precision@10, NDCG@10, MRR@10, Hit Rate@10) and a novel Large Language Model (LLM) based evaluation framework leveraging GPT-4o for semantic assessment of relevance and serendipity.

Results show that LightGCN consistently outperforms other models in both personalized and non-personalized scenarios, achieving the highest accuracy and scalability. Non-personalized recommendations remain valuable in cold-start and anonymous browsing contexts. The integration of LLM based evaluation offers deeper qualitative insights into recommendation quality, capturing semantic alignment and novelty beyond what is reflected in traditional metrics. The proposed framework demonstrates that a unified embedding based architecture can effectively serve heterogeneous recommendation needs on large-scale scholarly platforms. The methodology and findings have broader implications for the design of academic recommender systems in data sparse and mixed user environments.

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.

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.