Product Recommendation using Hadoop

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