PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining Predictions for an accurate Recommender System
Michael Jahrer, Andreas Töscher and Robert Legenstein
In: KDD 2010, Washington DC, USA(2010).

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:6084
Deposited By:Michael Pfeiffer
Deposited On:08 March 2010