A Friendbook Recommendation System for Social Networks  
  Authors : Pratidnya Gorade; Jyoti Diwate; Achyut Walse; Jagdish Khetre


Existing social networking sites like Facebook, Google+ etc. recommend friends to their users supported by their tastes and people they already perceive, that can`t replicate users’ reality preferences on friend selection. In this paper, we present a life style primarily based on friend recommendation system for social networks that recommends friends to users supporting their life designs rather than social graphs. By exploiting sensor-rich smartphones, this technique makes an endeavor to derive life kinds of users by exploitation data obtained from sensors that's terribly user-centric. It, in addition measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. It permits users to talk with friends. Galvanized by text mining, we tend to model a user’s manner as life documents; from that his/her life designs area unit are extracted by exploitation the Latent Dirichlet Allocation formula. We tend to additional propose a similarity metric to live the similarity of life designs between users, and calculate user’s impact in terms of life styles with a friend-matching graph. Upon receiving asking, system returns an inventory of people with highest recommendation scores to the question user. Finally, this model additionally integrates a feedback mechanism to boost the recommendation accuracy and users satisfaction.


Published In : IJCAT Journal Volume 3, Issue 1

Date of Publication : January 2016

Pages : 77 - 80

Figures :02

Tables : --

Publication Link :A Friendbook Recommendation System for Social Networks




Pratidnya K. Gorade : Student of B.E Computer Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.

Jyoti R. Diwate : Student of B.E Computer Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.

Jagdish K Khetre : Student of B.E Computer Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.

Achyut Walse : Student of B.E Computer Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.








Data Mining

Friend Recommendation




Machine Learning


In this paper, we presented the design of the Friend recommendation through linguistics primarily based matching and cooperative filtering System in social networks. That is totally different from the friend recommendation mechanisms wishing on social graphs in existing social networking services, this recommendation system takes the user connected knowledge collected from user and by exploitation that knowledge we tend to create the friend match graph and by exploitation that graph we tend to suggested potential friends to users if they share similar life styles? We tend to conjointly acquire the feedback and question from the user relating to sure issue in order that we are able to resolve the matter. We tend to conjointly obtain the feedback from the user regarding our suggested system. We tend to enforce our suggested system on the Android-based smart-phones, and evaluated its performance on each small-scale experiment. The results showed that the recommendations accurately replicate the preferences of users in selecting friends. Beyond the present model, the longer term work will be four-fold.










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