An Augmented Similarity Approach for Improving Collaborative Filtering based Recommender System

Collaborative Filtering (CF) is one of the earliest and extensively used techniques to provide recommendations to the users. The success of collaborative filtering can be attributed to its ability to find the similarity among users and identify a set of similar users known as neighborhood set. In this direction, many researchers have put their efforts to investigate and propose new and modified similarity measures. Some researchers have combined two measures or improved a traditional similarity measure with a method known as similarity modifier. In this paper, we analyze and evaluate various similarity measures and compare them with an augmented similarity measure approach. Our approach identifies the factor which improves the performance of a similarity measure. The experiments conducted on Yahoo! Movies dataset and the comparative results show that our proposed approach performs better than the state-of-the-art approaches in terms of Coverage, MAE and RMSE evaluation measures.