Social Clustering-based Similar User Indexing for Large Recommender System

The tremendous growth of data in recent years poses some key challenges for recommender systems. Theses keys are related with producing high quality recommendations and fast performing the composition recommended items. In this paper, we propose social clustering-based similar user index to not only improve the prediction of recommendations, but also compose personalized recommendations in fast. Through the experimental result, we show that proposed clustering method is more accurate than k-means which is prevalent clustering techniques. And, we reduce computation time needed for composing recommendation. That is, proposed clustering-based indexing method improves the performance of recommender systems which deals with a very large data.