Recommendation Engine

The Recommendation Engine project was designed to help users discover learning opportunities tailored to their individual needs and preferences, enabling self-paced learning and enhancing user engagement. The system leverages insights from Text Analysis experiments and the ESCO taxonomy to accurately match learning opportunities with user profiles.

Recommendations are presented through the Playlist Builder and different visualization dashboards, providing an intuitive and personalized way to explore relevant content. The prototype successfully demonstrated the integration of all components, showcasing the ability to generate recommendations based on user profiles. This project highlights the potential of combining advanced text analysis with structured taxonomies to deliver impactful and user-centric learning experiences.