AN EFFICIENT PERFORMANCE ANALYSIS USING COLLABORATIVE RECOMMENDATION SYSTEM ON BIG DATA

In all the technological fields, the data size increases very rapidly and also database becomes very bulk in size. Users using bulk databases confront several challenges, such as determining which query produces the most relevant results. As the number of users has increased dramatically in recent years, there have been various competitions for recommendation systems. For enhancing or building recommendation systems all most or commonly everybody come up with an idea of collaborative filtering technique. When database or the data size increases it also reflect the processing time consumed and as well as the proposals will have potential. It is the best errand to give proposal to huge scope issues to create high greatness suggestions. Nonetheless, several approaches for the expansion of the recommender framework have been presented. Perhaps, the most and famous popular framework in for modern large datasets is Map Reduce, due to the outstanding features as gullibility, fault-tolerance, ease and effective of programming, flexibility. This paper aims to state the enlightening the status of effective and parallel query processing using Apache Mahout, Map Reduce and collaborative filtering.