Genetic algorithm based efficient social network Recommender System

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-36 Number-4
Year of Publication : 2016
Authors : Sandeep Kaur, Mini Ahuja
  10.14445/22312803/IJCTT-V36P138

MLA

Sandeep Kaur, Mini Ahuja "Genetic algorithm based efficient social network Recommender System". International Journal of Computer Trends and Technology (IJCTT) V36(4):219-224 June 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The rapid development of the net has relatively increased the quantity of social web site users. At the same time frame there is a remarkable increase in demand of customers for unique web. A recently available statistical record suggests that the 70% of the internet traffic suffers from obstruction problem. Therefore evaluating the particular demand of provided site may possibly improve the efficiency of social sites. That paper shows that the use of hybridization of knowledge mining techniques can be done to enhance the precision rate more for social network advised system. Most of the present techniques are restricted to some significant top features of social networks. The employment or genetic algorithm has been dismissed to enhance the precision rate more for national networks. The precision rate of today's techniques can be acquired to be poor therefore development is necessary to cause them to become more regular. Therefore in order to reduce these constraints a fresh approach have been planned which will use genetic algorithm to boost the prediction rate further.

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Keywords
Social Networks, Recommender systems, Types of Recommendation system, Genetic algorithm, Random forest algorithm.