Comparative Analysis of Collaborative Filtering on GraphLab, MLlib and Mahout

Recommendation systems are used to recommend items or products to the user based on their previous purchases, visits, interests, ratings, wish-lists or reviews to develop interest and to display the accurate and suitable items on board. Recommendation systems are used in various online shops (E-Commerce application) and decision making systems. Recommendation is a particular form of information filtering. It falls under the Data Mining and Machine Learning. Collaborative Filtering is the key technique used in this system. In this study, the data loading, model generation, recommendation implementation and accuracy of same algorithm on some major tools and libraries (GraphLab, Mahout-Hadoop, Mahout-Spark and MLLib) has been discussed. To serve the purpose, a well-known algorithm Alternating Least Square ALS for collaborative filtering was used. Netflix Prize (training) data set was used in this research with the listed tools and libraries. At the end of this research a factual comparative analysis of the tools was carried out.