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IJCERT Journal ISSN(Online):2349-7084

International Journal
of Computer Engineering in Research Trends (IJCERT)

Scholarly, Peer-Reviewed, Open Access and Multidisciplinary


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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Social Network Based "FndSearch” Recommender Framework

Telaprolu Swamulu, P.Sujatha , , ,
Affiliations
M.Tech (CSE), Department of Computer Science & Engineering, NRI Institute of Technology</br>Associate Professor, Department of Computer Science & Engineering, NRI Institute of Technology
:NOT ASSIGNED


Abstract
Social networks give an essential origin of data with respect to clients and their cooperation’s which is exceptionally important for the recommender systems. In web-based interpersonal organizations, social trust connections between clients demonstrate the likeness of their needs and assessments. In this paper, we introduced a Social network based recommender framework named "FndSearch" an application that uses the data of the client and makes suggestions by considering client's real intrigue and figuring the likenesses between every client, consequently prescribing companions. A probabilistic model is being created to make this customized proposal from the fundamental data gathered from the client. We additionally help the clients in a manner via looking and prescribing companions who don't have a place with the same classification of the significant enthusiasm as the client.


Citation
Telaprolu Swamulu,P.Sujatha."Social Network Based "FndSearch” Recommender Framework". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 10,pp.847-852, October - 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I1010.pdf,


Keywords : Social networks, Recommender system, user interest, personalized recommendation.

References
[1] Prem Melville and Vikas Sindhwani, Recommendation Systems, In Encyclopedia of Machine Learning, Claude Sammut and Geoffrey Webb (Eds), Springer, 2010 Chapter No: 00338, Pg 829-838 
[2] W. H. Hsu, A. King, M. Paradesi, T. Pydimarri, and T. Weninger. Collaborative and structural recommendation of friends using weblog-based social network analysis. Proc. Of AAAI Spring Symposium Series, 2006. 
[3] Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi, and Zhi Wang, “Friendbook: A Semantic-based Friend Recommendation System for Social Networks”IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 99, MAY2014 
[4] 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. [5] A.Kirankumar, P.Ganesh Kumar Reddy, A.Ram Charan Reddy” A Logic-based Friend Reference Semantic System for an online Social Networks”, International Journal Of Computer Engineering In Research Trends Volume 1, Issue 6, December 2014, PP 501-506,www.ijcert.org. [6] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993- 1022, 2003. 
[7] L. Bian and H. Holtzman. Online friend recommendation through personality matching and collaborative filtering. Proc. of UBICOMM, pages 230- 235, 2011. 
[8] K. Farrahi and D. Gatica-Perez. Probabilistic mining of sociogeographic routines from mobile phone data. Selected Topics in Signal Processing, IEEE Journal of, 4(4):746-755, 2010. 
[9] 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. 
[10] L. Gou, F. You, J. Guo, L. Wu, and X. L. Zhang. Sfviz: Interestbased friends exploration and recommendation in social networks. Proc. of VINCI, page 15, 2011.


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h-index 14
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DOI:10.22362/ijcert