Product Recommendation using Hadoop

Main Article Content

Prof. Deepali Patil, Ujwal Ahir, Dhruv Bindoria, Shankarlal Bhati, Raj Mehta

Abstract

Recommendation systems are used widely to provide personalized recommendations to users. Such systems are used by e-commerce and social networking websites to increase their business and user engagement. Day-to-day growth of customers and products pose a challenge for generating high quality recommendations. Moreover, they are even needed to perform many recommendations per second, for millions of customers and products. In such scenarios, implementing a recommendation algorithm sequentially has large performance issues. To address such issues, we propose a parallel algorithm to generate recommendations by using Hadoop map-reduce framework. In this implementation, we will focus on item-based collaborative filtering technique based on user's browsing history, which is a well-known technique to generate recommendations.

Article Details

How to Cite
, P. D. P. U. A. D. B. S. B. R. M. (2018). Product Recommendation using Hadoop. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(4), 260–263. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1510
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