Collaborative Filtering Ensemble
This paper provides the solution of the team “commendo” on the Track1 dataset of the KDD Cup 2011 Dror etÂ al.. Yahoo Labs provides a snapshot of their music-rating database as dataset for the competition. We get approximately 260 million ratings from 1 million users on 600k items. Timestamp and taxonomy information are added to the ratings. The goal of the competition was to predict unknown ratings on a testset with RMSE as error measure. Our final submission is a blend of different collaborative filtering algorithms. The algorithms are trained consecutively and they are blended together with a neural network.