Product Recommendation using Multiple Filtering Mechanisms on Apache Spark

S.N. Patil¹ , S.M. Deshpande² , Amol D. Potgantwar³

¹ Dept. Computer Engineering, Sandip Institute of Technology and Research Centre, Nashik, India.
² Dept. Computer Engineering, Sandip Institute of Technology and Research Centre, Nashik, India.
³ Dept. Computer Engineering, Sandip Institute of Technology and Research Centre, Nashik, India.

Correspondence should be addressed to: shweta.patil@sitrc.org.

Section:Research Paper, Product Type: Journal
Vol.5 , Issue.3 , pp.76-83, Jun-2017

Online published on Jun 30, 2017

Copyright © S.N. Patil, S.M. Deshpande, Amol D. Potgantwar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Recommendation system is used by enormous users. Recommendations based on rating prediction provides useful recommendations for products, ratings are predicted using various methods. It is used commonly in recent years, and is used in variable areas in many popular applications which comprise of movies, songs, bulletin, files, research courses, online shopping, social networking sites, and product recommendation. Gradually, the amount of users, objects and facts has matured rapidly, the big data scrutiny problem for examination of recommender systems. Conventional recommender systems frequently suffer from scalability, efficiency and real time recommendation problems while processing or analyzing documents taking place at huge scale. To get rid from these problems, recommendation algorithms such as SVD, Trust SVD, and Incremental SVD are implemented on Apache Hadoop and Spark for evaluating most efficient recommendation. Analysis proves which prediction algorithm works more efficient than other algorithms. Proposed framework will provide real time and multiple recommendations to multiple users simultaneously.

Key-Words / Index Term :
Bigdata, Collaborative, Hadoop, Incremental SVD, MapReduce, Product, Recommendations, Spark, SVD, Trust SVD

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