In this paper, we propose two exponential similarity measures for collaborative filtering in recommender systems. The proposed similarity measures are used to estimate the distance between two users or items. Furthermore, an algorithm is proposed to use the distance obtained from similarity measures introduced in this paper to evaluate empty rating elements in the rating matrix and compare the results of these similarity measures with our exponential similarity measures. Therefore, we deduce that the algorithm using the exponential similarity measures works better than the algorithm using Manhattan, Euclidean, Jaccard, Cosine or Pearson similarity measures when taking K-Most-Similar-Users to evaluate the similarity in order to find ratings for the user that we want to predict ratings for.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.