Friend Recommendation System Using Map-Reduce and Spark: A Comparison Study

Abstract

Connecting with other people online is common practice and huge amounts of data are generated each day by increasing web activity. This type of data collection can be used to recommend friends on social media. We have utilized Map Reduce and Spark to analyze the vast amount of data. A Friend Recommendation system has been implemented using Map Reduce and Spark. Furthermore, we compared both Distributed Computation techniques in order to determine the optimum solution. We found that spark computation is 16 times faster than Hadoop Map-Reduce computation for Friend Recommendation System. Spark proves to be more efficient than map-reduce in terms of time efficiency.