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Generic Recommendation Engine using Spark

Author(s):

Saif Iqbal Shaikh , Jawahar Education Society's Institute of Technology, Management and Research; Kalpesh Sanjay Mali, Jawahar Education Society's Institute of Technology, Management and Research; Sharuk Ashpak Shaikh, Jawahar Education Society's Institute of Technology, Management and Research; Geetanjali P Mohole, Jawahar Education Society's Institute of Technology, Management and Research

Keywords:

Generic, Distributed, Spark, Machine Learning, Collaborative Filtering

Abstract

Nowadays information is increasing day by day and there is a tremendous increase in information available over the Internet has created a challenge in searching of useful information, therefore intelligent approaches are needed to provide users to efficiently locate and retrieve information from the Web. In recent times recommender systems, recommend everything from movies, books, music, restaurant, news to jokes. Collaborative filtering algorithms are one of the most successful recommendation techniques which present information on items and products that are according to user’s interest. There are two methods in Collaborative filtering, user-based Collaborative filtering and item-based Collaborative filtering. Former ends a certain users interests by ending other users who have similar interests whereas item based Collaborative filtering looks into a set of items rated by all users and a computes how similar they are to the target item under recommendation. This paper aims to develop a model by splitting the costly computations in Collaborative filtering algorithms into three Map-Reduce phases.

Other Details

Paper ID: IJSRDV5I20417
Published in: Volume : 5, Issue : 2
Publication Date: 01/05/2017
Page(s): 471-474

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