Homogenizing social networking with smart education by means of machine learning and Hadoop: A case study

In today's age of ever increasing use of internet, there are around 74% active internet users out of which 60% users contribute to social networking and most of them are students from the age group 16–30 1. If this young generation is targeted specifically towards educational activities keeping the same social networking environment in the background would create interest in students for educational activities and also yield productive results. Using Big Data analytics, machine learning and recommender system on the student data and activity would provide them with useful information and suggestions which would help them gain knowledge and make proper decisions to make their future in right direction. This can be implemented by creating a social-cum-educational portal with recommender systems, also data can be generated and displayed on the same place after analysis through recommenders. There is large amount of social, educational information generated on a rapid basis on the web which can be analysed and used for the betterment of the students and also the analysed information can be provided to the students based on their interests. Specific information to specific student can be provided. Use of such technology can reduce the gap between students and the information which can lead to their inherent development and success! However, most of the existing Social Recommender systems do not have good scalabilities which are unable to process huge volumes of data. Aiming to this problem we can design a social recommender system based on Hadoop and its parallel computing platform.