Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

A Better Semantic based Friend Recommendation System for Modern Social Networks

Print
PDF
 
Download Full Text
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 8
Year of Publication: 2016
Authors:
Akash Bhapkar, Kajal Fegade, Rahul Ahire, Chitra Chaudhary, A. M. Jagtap
10.5120/ijca2016912475

Akash Bhapkar, Kajal Fegade, Rahul Ahire, Chitra Chaudhary and A M Jagtap. A Better Semantic based Friend Recommendation System for Modern Social Networks. International Journal of Computer Applications 156(8):1-5, December 2016. BibTeX

@article{10.5120/ijca2016912475,
	author = {Akash Bhapkar and Kajal Fegade and Rahul Ahire and Chitra Chaudhary and A. M. Jagtap},
	title = {A Better Semantic based Friend Recommendation System for Modern Social Networks},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {8},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume156/number8/26726-2016912475},
	doi = {10.5120/ijca2016912475},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

People meet new people on social networks from across the world, eventually bringing the world closer. Existing social systems recommend friends according to person’s social graph which includes mutual friends and social connections, which may not be the case when being friends in real life. A better approach would be to get recommendations according to user’s life-style rather than just social graph. The proposed system provides an intelligent and automated way to predict user’s lifestyle according to his daily activities and interests by taking advantage of sensor-rich smart-phones and recommend friends with high similarity of lifestyles. The user’s data is stored in database and lifestyle is extracted using topic model. By constructing friend-matching graph, our system depicts the similarity of lifestyles between two users. Upon receiving a request, a list of people with highest recommendation scores is returned to the query user. Finally, system integrates a feedback mechanism to further improve the recommendation accuracy.

References

  1. Z. Wang, J. Liao, Q.Cao and H.Qi. ’Friendbook: A Semanticbased Friend Recommendation System for Social Networks’. IEEE, pages 538 - 551, 2015.
  2. L. Bian and H. Holtzman. Online friend recommendation through personality matching and collaborative filtering. Proc. of UBI-COMM, pages 230-235, 2011.
  3. 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.
  4. 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.
  5. K. Farrahi and D. Gatica-Perez. Discovering Routines from Largescale Human Locations using Probabilistic Topic Models. ACM Transactions on Intelligent Systems and Technology (TIST), 2(1), 2011.
  6. K. Farrahi and D. Gatica-Perez. Probabilistic mining of socio-geographic routines from mobile phone data. Selected Topics in Signal Processing, IEEE Journal of, 4(4):746-755, 2010.
  7. T. Huynh, M. Fritz, and B. Schiel. Discovery of Activity Patterns using Topic Models. Proc. of UbiComp, 2008.

Keywords

Friend Recommendation, text mining, lifestyle, mobile sensing, social networks