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A Single Strong Disagreement Ruins a Recommender : Improving Recommendation Accuracy with a Simple Statistic

by Jennifer Golbeck
Human-Computer Interaction ()


Research on the use of social trust relationships for collaborative filtering has shown that trust-based recommendations can outperform traditional methods in certain cases. This, in turn, lead to insights that tie trust to certain more subtle types of similarity between users which is not captured in the overall similarity measures normally used for making recommendations. In this study, we investigate the use these trust-inspired nuanced similarity measures directly for making recommendations. After describing previous research that identified these similarity statistics, we present an experiment run on two data sets: FilmTrust and MovieLens. Our results show that using a simple measure - the single largest difference between users - as a weight produces significantly more accurate results than a traditional collaborative filtering algorithm and in some cases also outperforms a model-based approach.

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