HCIL-2009-26

Golbeck, J.
October 2009
HCIL-2009-26
Research on the use of social trust relationships for collaborative ltering 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 identi ed these similarity statistics, we present an experiment run on two data sets: FilmTrust and Movie- Lens. Our results show that using a simple measure - the single largest di erence between users - as a weight produces signi cantly more accurate results than a traditional collaborative ltering algorithm and in some cases also outperforms a model-based approach.
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