2010 CombiningPredictionsforAccurate

• (Jahrer et al., 2010) ⇒ Michael Jahrer, Andreas Töscher, and Robert Legenstein. (2010). “Combining Predictions for Accurate Recommender Systems.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835893

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Applications: Data mining.
• General Terms: Algorithms, Measurement, Performance

Cited By

• http://scholar.google.com/scholar?q=%22Combining+predictions+for+accurate+recommender+systems%22+2010
• http://portal.acm.org/citation.cfm?id=1835893&preflayout=flat#citedby

Quotes

Author Keywords

Recommender Systems, Netflix, Supervised Learning, Ensemble Learning

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 CombiningPredictionsforAccurate	Michael Jahrer Andreas Töscher Robert Legenstein			Combining Predictions for Accurate Recommender Systems		KDD-2010 Proceedings		10.1145/1835804.1835893		2010