Combining Predictions for Accurate Recommender Systems

M. Jahrer, A. Töscher, and R. Legenstein


We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filtering (CF) algorithms, which include: SVD, Neighborhood Based Approaches, Restricted Boltzmann Machine, Asymmetric Factor Model and Global Effects. We show that linearly combining (blending) a set of CF algorithms increases the accuracy and outperforms any single CF algorithm. Furthermore, we show how to use ensemble methods for blending predictors in order to outperform a single blending algorithm. The dataset and the source code for the ensemble blending are available online.

Reference: M. Jahrer, A. Töscher, and R. Legenstein. Combining predictions for accurate recommender systems. In KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 693-702, New York, NY, USA, 2010. ACM.