We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
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

Personalized Marketing in Facebook using Hidden Markov Model

by Gayana Fernando, Md Gapar Md Johar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 24
Year of Publication: 2018
Authors: Gayana Fernando, Md Gapar Md Johar
10.5120/ijca2018916207

Gayana Fernando, Md Gapar Md Johar . Personalized Marketing in Facebook using Hidden Markov Model. International Journal of Computer Applications. 179, 24 ( Mar 2018), 13-18. DOI=10.5120/ijca2018916207

@article{ 10.5120/ijca2018916207,
author = { Gayana Fernando, Md Gapar Md Johar },
title = { Personalized Marketing in Facebook using Hidden Markov Model },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 179 },
number = { 24 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number24/29079-2018916207/ },
doi = { 10.5120/ijca2018916207 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:18.909665+05:30
%A Gayana Fernando
%A Md Gapar Md Johar
%T Personalized Marketing in Facebook using Hidden Markov Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 24
%P 13-18
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the current dynamic business world, each business is compelled to keep up with the state-of-the-art technologies to survive in the ever competitive market. With the dawn of the digital era, marketers were opened up to a brand new path of marketing strategies that allows them to not only manage their existing consumers but also to capture the world wide attention of potential consumers at a trivial cost. Social networks are now pronounced to be the next generation of consumer-centric interactions. This is proven further by the data released by popular social networks like Facebook regarding their consumer interactions. Big data analysis is another innovative technology concept used by businesses to recognize consumer buying patterns. Big data analysis has made path to web data mining. With the growth of social networks its profile holders have become the creators and distributors of business data. Due to this, social networks hold terabytes of raw data. With these massive advantages in line, web mining techniques and theory requires to be researched thoroughly to uncover the potential patterns and user access behaviors of these large amounts of online data. The major dilemma faced by researches in this field is the dynamic behavior of online data. To add to this dilemma the data is also quite unstructured. With these difficulties, the need to discover an efficient approach to mine unorganized and dynamic data in social networks is of utmost importance. This research aims to fill this research gap by introducing a framework that can be used to mine profile user data extracted from Facebook and use the knowledge in personalized marketing. This research paper explains framework build based on Hidden Markov Model to predict the next movement to handle the dynamic behavior of web data.

References
  1. D. Boyd and N. Ellison, "Social network sites: definition, history, and scholarship", IEEE Engineering Management Review, vol. 38, no. 3, pp. 16-31, 2010.
  2. The Facebook effect: the inside story of the company that is connecting the world", Choice Reviews Online, vol. 48, no. 04, pp. 48-2179-48-2179, 2010.
  3. A. Montgomery and M. Smith, "Prospects for Personalization on the Internet", SSRN Electronic Journal.
  4. Rennie and G. Zorpette, "The social era of the web starts now", IEEE Spectrum, vol. 48, no. 6, pp. 30-33, 2011.
  5. M. Hao, H. Yang, M. R. Lyu, and I. King. "Mining social networks using heat diffusion processes for marketing candidates selection." Proceedings of the 17th ACM conference on Information and knowledge management, pp. 233-242. ACM, 2008.
  6. Tucker, E Catherine. "Social networks, personalized advertising, and privacy controls." Journal of Marketing Research 51, no. 5 (2014): 546-562.
  7. D. Ediger, K.. Jiang ,J. Riedy D. A. Bader, C. Corley, R. Farber, W. N. Reynolds, “ Massive Social Network Analysis: Mining Twitter for Social Good”, IEEE 39th International Conference on Parallel Processing, pp 583-593, 2010.
  8. S. Abrol, L. Khan, “TweetHood: Agglomerative Clustering on Fuzzy k- Closest Friends with Variable Depth for Location Mining”, IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust, pp 153-160, 2010.
  9. J. Chen, J. Fagnan, R. Goebel, R. Rabbany, F. Sangi, M.Takaffoli, E. Verbeek, O. Za¨ıane, “Meerkat: Community Mining with Dynamic Social Networks” , IEEE International Conference on Data Mining Workshops, pp 1377-1380, 2010.
  10. N. Koochakzadeh, A. Sarraf , K. Kianmehr , J. Rokne, R. Alhajj, “ NetDriller: A Powerful Social Network Analysis Tool”,11th IEEE International Conference on Data Mining Workshops, pp 1235-1238, 2011.
  11. H. Asadi, C. M˚artenson, P. Svenson, M. Sk¨old,, “The HiTS/ISAC Social Network Analysis Tool”, IEEE European Intelligence and Security Informatics Conference, pp 291-296, 2012.
  12. M. Bilgic , L. Getoor . B. Shneiderman, “D-Dupe: An Interactive Tool for Entity Resolution in Social Networks”, IEEE Symposium on Visual Analytics Science and Technology, pp 43-50 2006.
  13. Xue, Wei, JuWei Shi, and Bo Yang. "X-rime: cloud-based large scale social network analysis." Services Computing (SCC), 2010 IEEE International Conference on. IEEE, 2010.
  14. Wikipedia, 'Hidden Markov model', 2015. [Online]. Available: http://en.wikipedia.org/wiki/Hidden_Markov_model. [Accessed: 14- Apr- 2015].
  15. Centrifugesystems.com, 2018. [Online]. Available: http://centrifugesystems.com/. [Accessed: 14- Feb- 2018].
  16. "Cuttlefish - Network visualization and workbench", Cuttlefish.sourceforge.net, 2018. [Online]. Available: http://cuttlefish.sourceforge.net/. [Accessed: 14- Feb- 2018].
  17. Da Silva, G. Aires, and D. R. Ferreira. "Applying hidden Markov models to process mining." Sistemas e Tecnologias de Informação: Actas da 4ª Conferência Ibérica de Sistemas e Tecnologias de Informação, AISTI/FEUP/UPF. 2009.
  18. M. Zaki, C. Carothers and B. Szymanski, 'VOGUE', ACM Transactions on Knowledge Discovery from Data, vol. 4, no. 1, pp. 1-31, 2010.
  19. Kemeny, John G., and James Laurie Snell. "Finite Markov Chains, Undergraduate Texts in Mathematics." (1976).
  20. Eirinaki, Magdalini, and Michalis Vazirgiannis. "Web mining for web personalization." ACM Transactions on Internet Technology (TOIT) 3.1 (2003): pp 1-27.
  21. Crnovrsanin, T., Muelder, C., Correa, C., & Ma, K. L. (2009, October). Proximity-based visualization of movement trace data. In Visual Analytics Science and Technology, 2009. VAST 2009. IEEE Symposium on (pp. 11-18). IEEE.
  22. Chang, Peng, Mei Han, and Yihong Gong. "Extract highlights from baseball game video with hidden Markov models." Image Processing. 2002. Proceedings. 2002 International Conference on. Vol. 1. IEEE, 2002.
  23. G. Fernando, M. Gapar and M. MdJohar, "Framework for Social Network Data Mining", International Journal of Computer Applications, vol. 116, no. 18, pp. 7-10, 2015.
  24. B. Stone, "Advertising on Facebook Strikes Some as Off-Key", Nytimes.com, 2017. [Online]. Available: http://www.nytimes.com/2010/03/04/technology/04facebook.html?_r=0. [Accessed: 01- Jun- 2017].
Index Terms

Computer Science
Information Sciences

Keywords

Social Networks Web Data Mining Personalized Marketing Hidden Markov Model

Powered by PhDFocusTM