Combining predictions for accurate recommender systems

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 [9]. Categories and Subject Descriptors H.2.8 [Database Applications]: [Data mining] General Terms Algorithms, Measurement, Performance Keywords Recommender Systems, Netflix, Supervised Learning, Ensemble Learning

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Michael Jahrer, Andreas Töscher, Robert Legenstein
• Keywords: Cf Algorithm, Data Mining, Ensemble Learning, KDD 2010, Recommender Systems