Recommender Systems

• Content-based Filtering
• Collaborative Filtering
• Exploration vs. Exploitation
• Evaluation Methodology and Metrics
• Personalization
• Context-awareness
• Natural Language Processing
• Ethical Considerations
• Generative AI
• Multi-objective Recommendation

Knowledge: Recommendation algorithms, including content-based filtering and collaborative filtering. Evaluation methodology and a variety of metrics. Critical reflection of ethical aspects. Considering contextual information. Feature engineering with natural language processing technology. Optimization for multiple criteria.

Skills: Statistical methods to analyze the output of recommender systems. Implementing scalable systems.

General competence: Critical thinking. Teamwork. Problem solving.

Lectures, exercises and group project.

The course is taught in English.