Context-aware recommender systems

Recommender systems try to predict users' preferences for certain items, given a set of historical data. Multiple dierent techniques are available that make these systems accurate and one of them that delivers promising results is matrix factorization. This thesis explores how these systems work and presents a method to incorporate contextual data into a factorization technique to get predictions based on context. Specically, a music recommender based on Candecomp/Parafac tensor factorization is .