Abstract:

The collaborative filtering approach to recommender systems predicts user
preferences for products or services by learning past user-item
relationships. Their significant economic implications made collaborative
filtering techniques play an important role at known e-tailers such as
Amazon and Netflix. This field enjoyed a surge of interest since October
2006, when the Netflix Prize competition was commenced. Netflix released a
dataset containing 100 million anonymous movie ratings and challenged the
research community to develop algorithms that could beat the accuracy of its
recommendation system, Cinematch. In this talk I will survey the competition
together with some of the principles and algorithms, which have led us to
winning the Progress Prizes in the competition.