A Friend Recommender System for Social Networks by Life Style Extraction Using Probabilistic Method -"Friendtome"
Namrata M. Eklaspur, Anand Pashupatimath, M. Tech, P. G. Student, Dharwad India · 2015
Every day we are overwhelmed with many choices and options, simultaneously recommendation systems have gained popularity in providing suggestions. Today every web application has its own recommendation system. Whereas, Recommendation systems for social networks are different from other kinds of system, since the item here are rational human beings rather than goods. Hence, the ‘Social’ factor has to be accounted for when making a recommendation. We considered one of the most popular social Networking sites that is Facebook as it offers impressive features. Here, we are mainly focusing on recommending friend with similar interest which is different among all the existing ones where Facebook uses social graph a friend of friend approach to recommend friend which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. And Netflix, Foursquare which all focus on recommending items. Hence we proposed framework Friendtome, a novel semantic based friend recommendation system for social networks. In this paper, a social network is formally represented and taking text mining as a perspective, we have proposed a framework that will recommend friend using an efficient Algorithm. Here, we have analyzed the structure of Facebook and considering the activities of individuals got some values & computed the score of each individual based on which we have, analyzed and computed to show the percentage of similarity of life styles between users, and recommends friends to users if their life styles have high similarity.