Performance and Quality Assessment of Similarity Measures in Collaborative Filtering Using Mahout

Recommendation systems use knowledge discovery and statistical methods for recommending items to users. In any recommendation system that uses collaborative filtering methods, computation of similarity metrics is a primary step to find out similar users or items. Different similarity measuring techniques follow different mathematical approaches for computation of similarity. In this paper, we have analyzed performance and quality aspects of different similarity measures used in collaborative filtering. We have used Apache Mahout in the experiment. In past few years, Mahout has emerged as a very effective and important tool in the area of machine learning. We have collected the statistics from different test conditions to evaluate the performance and quality of different similarity measures.