Combining Predictions for Accurate Recommender Systems

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

Abstract:

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.