Enhancing E-Commerce Recommendations with Machine Learning and Graph Databases - Melnychenko, Hlib (2025)
This thesis tackles the critical challenge of delivering relevant product recommendations in e-commerce environments where data sparsity and "Cold-Start" users (those with no history) render traditional algorithms ineffective. To solve this, the work proposes and implements a novel Inductive Hybrid Recommendation System. This architecture uniquely combines structural learning with deep semantic understanding, utilizing GraphSAGE—an inductive Graph Neural Network—to model complex user-item relationships, and Sentence-BERT (SBERT) to generate rich semantic embeddings from unstructured product text. These features are synthesized by a supervised XGBoost Ranker, optimized for a Pairwise Ranking objective to ensure high-quality list ordering. Evaluated on a large-scale Amazon Reviews dataset using a strict chronological split to prevent data leakage, the hybrid model demonstrated superior robustness. While a traditional Content-Based Baseline failed completely for new users (0% success), the Hybrid model achieved a 15% Hit Rate for cold-start users, effectively solving the zero-start problem. Simultaneously, it outperformed the baseline for established users with an 84.6% improvement in ranking quality (nDCG). Beyond quantitative success, the system leverages the Neo4j graph database to visualize decision pathways, providing interpretability and trust in the recommendations. The thesis concludes by outlining a deployment roadmap and proposing future enhancements such as Multi-Modal Feature Fusion using CLIP to integrate visual data for even richer discovery.