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 proposed that uses implicit
feedback collected from music listeners. The results are empirically
tested and compared with other non-contextual recommender techniques.
The prediction quality of the matrix factorization technique is unfortunately
not improved by our proposed tensor factorization recommender on the used
Last.fm dataset. However, an adjusted dataset with articially made contextual
data does get better results, but this may not re
ect a real-world
situation.