Recommender system for computer componentsHerzog, Aljaž (Avtor) Podgorelec, Vili (Mentor) recommender systemcomputer componentshybrid recommender systemmachine learningThis thesis explores the implementation of a personalized and accurate recommender system for computer components, addressing key challenges within the e-commerce sector. A full-stack solution was developed, featuring a React-based frontend, a Flask backend, and a MongoDB database. The system integrates and evaluates four distinct recommendation algorithms: a basic model, Content-Based Filtering (CBF), Collaborative Filtering (CF), and a hybrid approach. Evaluation metrics revealed that CBF provided the highest accuracy among the individual methods. The hybrid system's performance matched that of the CBF model, primarily due to insufficient user interaction data limiting the effectiveness of the CF component. This outcome underscores the critical need for comprehensive datasets to fully leverage the power of collaborative and hybrid recommendation systems.[A. Herzog]20252025-09-01 20:39:25Magistrsko delo/naloga95011sl