CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

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

by Chinar Bhandari, M. D. Ingle
journal cover thumbnail

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
  1. Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi, and Zhi Wan, “Friendbook: A Semantic-based Friend Recommendation System for Social Networks”, pages 1-14, 2015.
  2. Liang Hu, Guohang Song, Zhenzhen Xie, and Kuo Zhao, ”Personalized Recommendation Algorithm Based on Preference Features”, pages 293-299, 2014.
  3. Yongkun Li, and John C. S. Lui, ”Friends or Foes: Distributed and Randomized Algorithms to Determine Dishonest Recommenders in Online Social Networks”, pages 1695-1708, 2014.
  4. Malmaz Roshanaei, Shivakant Mishra,”An Analysis of Positivity and Negativity Attributes of Users in Twitter”, pages 1-6, 2014.
  5. Shunmei Meng, Wanchun Dou, Xuyun Zhang and Jinn Chen,”KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications”, pages 1-11, 2013.
  6. Face book statistics. http://www.digitalbuzzblog.com/ facebook-statistics-stats-facts-2011/.
  7. L. Bian and H. Holtzman. Online friend recommendation through personality matching and collaborative filtering. Proc. of UBICOMM, pages 230-235, 2011.
  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.

Powered by PhDFocusTM