Ontology-Based E-Commerce Recommender System: A Hybrid Semantic Filtering Approach

The rapid growth of e-commerce has led to product overload, which has resulted in personalized product discovery becoming a crucial problem for consumers. Classic recommender systems (RS), which rely heavily on content-based filtering or collaborative filtering, tend to face well-known problems such as cold start, data sparsity, and ignorance of semantics. Such constraints frequently result in irrelevant or redundant suggestions, thereby lowering the user satisfaction and conversions. This study presents a hybrid ontology-based e-commerce recommender system that combines symbolic reasoning with deep semantic matching. The system is based on a Neo4j graph database to capture structured product relationships and is combined with sentence embedding models (MiniLM) to compute the semantic similarity between user queries and product data. For semantic matching, cosine similarity is used, and for ontology-based filtering, graph relationships, such as SAME_CATEGORY, SIMILAR_PRICE, and SAME_MANUFACTURER, are employed. An e-commerce dataset that was cleaned and pre-processed was used to test the system. The performance was measured using the following metrics: precision, Recall, F1-Score, and accuracy. The performance was measured using the following metrics: precision, Recall, F1-Score, and accuracy. The proposed system achieved a precision of 0.95, recall of 0.93, F1-Score of 0.94, and accuracy of 0.94, demonstrating that the hybrid approach yields superior recommendation quality compared with using a single method.