Multi-criteria clustering-based recommendation using Mahalanobis distance

There have been significant advances made in the research of recommender systems over the past decades and have been implemented in both industry and academia. Recently, multi-criteria ratings are being incorporated into traditional recommender systems to further improve their quality, especially to handle the data sparsity and cold start issues. However, incorporation of multi-criteria ratings have improved the performance of the recommendation, but at the same time, multi-dimensionality issue also arises. This paper presents a clustering-based recommendation approach which is used for dealing with the multi-dimensionality issue in multi-criteria recommender systems. Here, we cluster the users based on their individual criteria ratings using K-means clustering and the intra-cluster similarity is computed using Mahalanobis distance measure for neighbourhood set generation. This improves the recommendations quality and predictive accuracy of both traditional and clusteringbased collaborative recommendations. The Yahoo! Movies dataset was used for testing the approach and the experiment conducted shows promising results.