Mapreduce in Social Networking with Smart Education System using Machine Learning

Balaji Bodkhe, Sanjay P. Sood


One of the marvels of the today’s world which is
shaping the future, is the worldwide accessibility to the internet.
There are around 7.2 billion people in the world out of which
there are about 3.1 billion active internet users out of which 65%
of the users devote it towards social networking & majority of
them are students from the age group of 16-30 [1]. If this
developing thoughts are targeted specifically towards informative
& academic actions keeping the same social networking
environment in the background would create interest in students
for educational activities and also develop productive reports.
Using Big Data analytics, machine learning and recommender
system on the pupils data and activity would nurture them with
useful information and suggestions which would help them yield
knowledge and make them choose their future in right resolution.
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 a humongous quantity of social,
educational data being generated at a great rate on the web
which can be examined and used for the betterment of the
students and also the analysed information can be delivered to
the students based on their interests. Particular information to
particular student can be provided. Use of such a system will
strengthen the educational activities amongst students that would
lead to maximize their success ratio! However, most of the
existing Social Recommender systems do not have good
extensibility 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.

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Jeff Bullas, (2015)."33 Social Media Facts and Statistics You Should Know in

". Available:


Kadie Regan, (2015)."10 Amazing Social Media Growth Stats From

". Available:


Chaobo He, Yong Tang , Zhenxiong Yang, Kai Zheng and Guohua

Chen(2014, June.). SRSH: A Social Recommender System based on

Hadoop. International Journal of Multimedia and Ubiquitous

Engineering Vol.9, No.6 (2014), pp.141-152. Available:

Sachin Sharma, Diksha Sharma, Pankaj Vaidya(2014, June.). Analytics in

Education Using Big Data. International Journal of Advanced Research in

Computer Science and Software Engineering Volume 4, Issue 11, November 2014.



Jamali,M.;Abolhassani, H(2006 Dec.). Different Aspects of Social

Network Analysis. Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM

International Conference.

Verma, J.P; Patel, B. ; Patel, A(2015 Feb.). Big Data Analysis:

Recommendation System with Hadoop Framework Computational

Intelligence & Communication Technology (CICT). 2015 IEEE

International Conference .

Xiaoyue Han; Lianhua Tian; Minjoo Yoon; Minsoo Lee(2012 Nov.). A

Big Data Model Supporting Information Recommendation in Social

Networks. Cloud and Green Computing (CGC), 2012 Second

International Conference.

Bamnote,G.R.;Dept. of CSE, PRMITR, Amravati, India ;

Agarwal.S.S(2015 Feb.). Evaluating and Implementing Collaborative

Filtering Systems Using Apache Mahout. in Hybrid Intelligent Systems

(HIS), 2011 11th International Conference.

Kwanghee Hong; Hocheol Jeon ; Changho Jeon(2012 Aug.).

UserProfile-based personalized research paper recommendation system.

Computing and Networking Technology (ICCNT), 2012 8th

International Conference.

“Introduction to Apache Mahout .” Available:

Mohamed Amine Chatti; Simona Dakova; Hendrik Thus, and Ulrik

Schroeder(2013 June.). Tag-Based Collaborative Filtering

Recommendation in Personal Learning Environments. LEARNING


Dr. Jaideep Srivastava, (2008). Data mining for social network analysis

Intelligence and Security Informatics, 2008. ISI 2008. IEEE

International Conference.

Tewari, A.S. ; Ansari, T .S. ; Barman, A.G(2014,Nov.). Opinion based book

recommendation using Naive Bayes classifier. Contemporary Computing and

Informatics (IC3I), 2014 International Conference.

Meller, T . ; Wang, E. ; Fuhua Lin ; Chunsheng Yang(2009 Feb.). New

Classification Algorithms for Developing Online Program

Recommendation Systems. Mobile, Hybrid, and On-line Learning,


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