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FriendFinder: A Lifestyle based Friend Recommender App for Smart Phone Users

by Chinar Bhandari, M. D. Ingle
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 6
Year of Publication: 2016
Authors: Chinar Bhandari, M. D. Ingle
10.5120/ijca2016910711

Chinar Bhandari, M. D. Ingle . FriendFinder: A Lifestyle based Friend Recommender App for Smart Phone Users. International Journal of Computer Applications. 145, 6 ( Jul 2016), 5-10. DOI=10.5120/ijca2016910711

@article{ 10.5120/ijca2016910711,
author = { Chinar Bhandari, M. D. Ingle },
title = { FriendFinder: A Lifestyle based Friend Recommender App for Smart Phone Users },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 6 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number6/25280-2016910711/ },
doi = { 10.5120/ijca2016910711 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:02.161577+05:30
%A Chinar Bhandari
%A M. D. Ingle
%T FriendFinder: A Lifestyle based Friend Recommender App for Smart Phone Users
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 6
%P 5-10
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today’s Social Networking services focuses towards suggesting you friends based on users social graph or Geo-location based, which neither take users life style into account or users liking ,disliking etc. Suggesting friends using the users’ link analysis may not be the best preference of suggestion for the users. In this paper, we present FriendFinder, a reliable user relation based friend suggesting app which recommends friend list to app users based on their analysis of life style and daily curricular activities on mobile phone instead of social graphs. FriendFinder captures users data i.e. daily activities and work done through mobile, for ex: App Usage, App Frequency, Browser Activities etc. Then we create a user profile with all gathered data and find most relevant matching profiles of existing candidate friends matching our profile for similarity and suggesting the result out of similarity test to the user as a friend.

References
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  2. Liang Hu, Guohang Song, Zhenzhen Xie, and Kuo Zhao, ”Personalized Recommendation Algorithm Based on Preference Features”, pages 293-299, 2014.
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  8. J. Kwon and S. Kim. Friend recommendation method using physical and social context. International Journal of Computer Science and Network Security, 10(11):116-120, 2010.
  9. X. Yu, A Pan, L.-A. Tang, Z. Li, and J. Han. Geo-friends recommendation in gps-based cyber-physical social network. Proc. Of ASONAM, pages 361-368, 2011. query and post query approaches for classifying deep-web forms to further improve the accuracy of the form classifier.
Index Terms

Computer Science
Information Sciences

Keywords

Friend recommendation mobile sensing life style social networks app usage app frequency browseractivities categories.

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