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Reseach Article

Friend Recommendation System for Online Social Networks

by Dagadu M. Jadhavar, V. R. Chirchi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153 - Number 12
Year of Publication: 2016
Authors: Dagadu M. Jadhavar, V. R. Chirchi
10.5120/ijca2016911963

Dagadu M. Jadhavar, V. R. Chirchi . Friend Recommendation System for Online Social Networks. International Journal of Computer Applications. 153, 12 ( Nov 2016), 1-3. DOI=10.5120/ijca2016911963

@article{ 10.5120/ijca2016911963,
author = { Dagadu M. Jadhavar, V. R. Chirchi },
title = { Friend Recommendation System for Online Social Networks },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 153 },
number = { 12 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume153/number12/26538-2016911963/ },
doi = { 10.5120/ijca2016911963 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:58:55.180658+05:30
%A Dagadu M. Jadhavar
%A V. R. Chirchi
%T Friend Recommendation System for Online Social Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 153
%N 12
%P 1-3
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Already social networking services recommends friends list to requesting user that is based on their social graphs, but they cannot fulfill user need to user preferences on friend selection. In this paper, we represent Friend recommendation, as lifestyle based friend recommendation system for social networks. Collecting data from smartphone sensors and identifying lifestyle of user, if lifestyle having high score similarities then recommend friend to user. Here probability Distribution algorithm is used for extracting lifestyle of users learns from text mining. Also use the Activity recognition for classify the activity of user and propose a similarity metric to find the similarity of life styles between users, and after this one finding user’s impact in consideration of life styles with a friend-matching graph. User request is received then friend recommendation system gives response as similar lifestyle matching friends lists to user query. At last, Friend recommendation system uses the feedback modules to improve the recommendation accuracy. We have Friend recommendation system is to implemented on the Android-based smartphones, By using Friend recommendation system user can get best friend list for preferences of users in choosing friends.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Life style Friend Bag of Activity model Social Networks Similarity Metric.