Alternative Formulas for Rating Prediction Using Collaborative Filtering

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 neighborhoodbased collaborative filtering, and it is general and applicable to establishing relationships among agents with a common list of items which establish their preferences.

• Type: Conference
• Year: 2009
• Where: ISMIS
• Authors: Amar Saric, Mirsad Hadzikadic, David Wilson
• Keywords: Artificial Intelligence, Collaborative Filtering, ISMIS 2009, Neighborhood-based Collaborative Filtering, Traditional Neighborhood-based Metrics