The students will:

1. familiarize themselves with the concepts of Recommender Systems,
2. understand the challenges involved,
3. be able to “recommend” appropriate techniques when faced with a recommendation task, and
4. acquire hands-on experience implementing existing methods and evaluating them over real datasets. 

• Introduction
• Content-based Recommendations
• Collaborative Filtering
• Model-based CF -- Matrix Factorization
• Evaluation Methods
• Special Topics (Group Recommenders, Social Recommenders, e-Tourism domain)