Publications

Abstract : Collaborative Filtering (CF) is a prominent approach to ensure personalized recommendations to active online users. An efficient CF is the memory-based strategy that finds nearest neighbours to an active user using conventional similarity measures. Most such measures deal with a co-rated item rated by a pair of users and hence they are not appropriate to provide an effective recommendation to a sparse dataset having less co-rated items. This study proposes a novel similarity measure, Matusita coefficient in CF (MCF), which considers all ratings given by a user to estimate nearest neighbours. MCF considers local and global rating information provided by users on different rating scales. The performance of the proposed measure is examined and checked by comparing it to conventional measures using popular benchmark datasets like MovieLens and Netflix. The recommendation results demonstrate that the proposed measure outperforms conventional similarity measures on various performance metrics like Mean Absolute Error, Root Mean Squared Error, accuracy, precision, recall and coverage.