Enforcing recommendation in Social networks By user interests

Abstract

It is a human tendency to get others opinion before doing something, this we can see much more in friends circle. As the internet spreads over the world it interferes much more in our daily life, for example applications like Facebook and whatsup becomes the inseparable part of internet savvy peoples. So it is obvious that most of the users search their friends based on their taste or keywords using the recommendation system. Many recommendation systems are existed which provides the recommendation by capturing users taste or profile data in the social networking site. This paper put forwards an idea of creating recommendation system based on the collected user comment data from the social networking site pages using an efficient web crawler. This method enhances to get the recommendation from many social networking sites in a given instance. This makes the system as an independent adaptive model which can be easily apply on many social networking sites to get user recommendation for the given query. System strongly empowered by the well grained NLP protocols with fuzzy classification approach.
DOI: 10.17762/ijritcc2321-8169.150840