Personalized E Commerce Recommendations Using Semantic Web Intelligence and Fuzzy Logic

Traditional e-commerce recommendation systems through content-based and collaborative filtering face a set of drawbacks which request personalized solutions to enhance user experience. This research develops a combined recommendation system which incorporates Semantic Web Intelligence and Fuzzy Logic to achieve better precision and flexibility in recommendations. The Semantic Web Intelligence system builds organized linkages between users and products and their attributes using ontologies and RDF together with knowledge graphs for delivering context-sensitive suggestions. Fuzzy Logic accepts and processes imprecise user preferences through its fuzzy rule-based inference system which links user product and attribute interactions using a knowledge graph to perform dynamic fuzzy-based scoring of recommendations. The new evaluation method combining accuracy and diversity and relevance measures generates enhanced performances which outperform conventional approaches. The research shows its practical capabilities after case studies with additional proof from user trials. Several upcoming improvements involve the integration of deep learning mechanisms together with real-time fuzzy rule updating procedures and blockchain capabilities for implementing secure dynamic user preferences. The proposed recommendation system enhancement combines semantic reasoning with uncertainty-aware decision-making to create next-generation e-commerce platforms which focus on user needs.