Alternative Formulas for Rating Prediction Using Collaborative Filtering

Authors:
Amar Saric;Mirsad Hadzikadic;David Wilson
Affiliations:
College of Computing and Informatics, The University of North Carolina at Charlotte, Charlotte, USA 28223;College of Computing and Informatics, The University of North Carolina at Charlotte, Charlotte, USA 28223;College of Computing and Informatics, The University of North Carolina at Charlotte, Charlotte, USA 28223
Venue:
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Year:
2009

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Abstract

This paper proposes and evaluates several alternate design choices for common prediction metrics employed by neighborhood-based collaborative filtering approach. It first explores the role of different baseline user averages as the foundation of similarity weighting and rating normalization in prediction, evaluating the results in comparison to traditional neighborhood-based metrics using the MovieLens data set. The approach is further evaluated on the Netflix movie data set, using a baseline correlation formula between movies, without meta-knowledge. For the Netflix domain, the approach is augmented with a significance weighting variant that results in an improvement over the original metric. The resulting approach is shown to improve accuracy for neighborhood-based collaborative filtering, and it is general and applicable to establishing relationships among agents with a common list of items which establish their preferences.