Collaborative filtering based Recommender System is one of the most common technique used for personalized product ranking. It aids the consumer in decision-making process. It helps to choose a product according to the consumer's preference from a large pool of choices.Despite its success, collaborative filtering suffers from the sparsity problem which limits the quality of recommendations. In this paper, we investigate the application of clustering collaborative framework. A unique centroid selection approach for k-means clustering algorithm is proposed that aims to improve clustering quality. The results on three benchmark datasets depict the improvement in the quality of recommendations made. © 2017 Elsevier Ltd