Racecar: Ratings and Content Employed to Calculate Accurate Recommendations

Abstract

Racecar is a recommender system for movies that combines collaborative filtering-based, feature-based, and tag-based recommendations. A simple linear model (SlopeOne) is used for the collaborative filtering module, while the other two sub-systems are naive Bayes classifiers. In order to derive the best system, the modules are tested both alone and in combination. The lowest error (MAE 0.9328, RMSE 1.214) results from putting 90% weight on the collaborative filtering module and 10% weight on the feature-based recommendation module

    Similar works