Ontology Based Recommendation System
Priyanka Kaushik, Priyanka Rawat, Abhishek Kumar Gupta, A S Chithra, Ajay Kumar, Reeya Ottalwar · 2024
E-commerce platforms face the challenge of providing personalized product recommendations to users, a crucial factor in enhancing user satisfaction and increasing sales. This paper presents an ontology-driven recommendation system de- signed specifically for e-commerce websites to address this challenge. Ontologies provide a structured framework for organizing knowledge within a domain, facilitating more precise and efficient recommendation strategies. We employ a hybrid recommendation approach that combines collaborative filtering with content-based filtering methodologies. Collaborative filtering analyzes user actions and preferences to identify similar users and suggest items they have shown interest in. In contrast, content-based filtering focuses on the attributes and features of products to recommend items that align with the user’s preferences. To demonstrate the effectiveness of our ontology-based recommendation system, we conducted experiments using real- world e- commerce data. The results indicate that our system outperforms traditional recommendation techniques in terms of accuracy and user satisfaction.