K-means Atkinson clustering approach for collaborative filtering-based recommendation system
Surya Kant, Tripti Mahara · International Journal of Intelligent Systems Design and Computing · 2017
The amount of information generated by web is growing rapidly every day and results in information overload. The information overload problem makes recommendation system necessary. Collaborative filtering is one of the most successful approaches to design a recommendation system. The key idea of this technique is based on the common interest of users. If the user has similar taste in past for a set of items, then they will share common taste in future. However, sparsity and scalability are major drawbacks of this prosperous approach affecting the quality of recommendations. In this paper, a clustering-based recommendation algorithm is proposed. The clustering technique has been used to form neighbourhoods (groups of users who have similar preferences) of active user. It exploits underlying data correlation structures to choose the initial centroid for k-means. The experimental results on three benchmark datasets, MovieLens 100k, MovieLens 1M and Jester, demonstrates that proposed method exhibits superior accuracy in comparison to the traditional k-means based recommender systems.