Product Recommendation using Hadoop
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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.
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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 http://www.ijfrcsce.org/index.php/ijfrcsce/article/view/1510
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