CFP last date
22 April 2024
Reseach Article

Group Coupon Recommendation System for Mobile Users

by M. Thenmozhi, T. P. Ezhilarasi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 10
Year of Publication: 2016
Authors: M. Thenmozhi, T. P. Ezhilarasi
10.5120/ijca2016910379

M. Thenmozhi, T. P. Ezhilarasi . Group Coupon Recommendation System for Mobile Users. International Journal of Computer Applications. 143, 10 ( Jun 2016), 31-36. DOI=10.5120/ijca2016910379

@article{ 10.5120/ijca2016910379,
author = { M. Thenmozhi, T. P. Ezhilarasi },
title = { Group Coupon Recommendation System for Mobile Users },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 10 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number10/25115-2016910379/ },
doi = { 10.5120/ijca2016910379 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:46:03.100870+05:30
%A M. Thenmozhi
%A T. P. Ezhilarasi
%T Group Coupon Recommendation System for Mobile Users
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 10
%P 31-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommendation systems have become an essential component of online marketing. Group recommendation is a challenging issue due to the diversity and dynamics involved in the groups. The existing works in group recommendation mainly focused on content interest of group members ignoring other characteristic useful for improving the recommendations. In this paper, a group-coupon recommendation system has been proposed. It recommends location sensitive products to customers and helps them to form groups in order to avail the discount provided by the sellers in a group purchase. As the usage of smart phones has increased, a mobile application called promoterApp has been developed based on the proposed recommendation approach. Experiments were conducted for WhatsApp users to recommend group discounts.

References
  1. Chen, Y.L., Cheng, L.C. and Chuang, C.N., 2008. A group recommendation system with consideration of interactions among group members. Expert systems with applications, 34(3), pp.2082-2090
  2. Kim, J.K., Kim, H.K., Oh, H.Y. and Ryu, Y.U., 2010. A group recommendation system for online communities. International Journal of Information Management, 30(3), pp.212-219.
  3. Park, M.H., Hong, J.H. and Cho, S.B., 2007. Location-based recommendation system using bayesian user’s preference model in mobile devices. In Ubiquitous Intelligence and Computing (pp. 1130-1139). Springer Berlin Heidelberg.
  4. Pera, M.S. and Ng, Y.K., 2013. A group recommender for movies based on content similarity and popularity. Information Processing & Management,49(3), pp.673-687.
  5. Baatarjav, E.A., Phithakkitnukoon, S. and Dantu, R., 2008, November. Group recommendation system for facebook. In On the Move to Meaningful Internet Systems: OTM 2008 Workshops (pp. 211-219). Springer Berlin Heidelberg.
  6. Bobadilla, J., Ortega, F., Hernando, A. and Gutiérrez, A., 2013. Recommender systems survey. Knowledge-Based Systems, 46, pp.109-132.
  7. Kim, J.K., Kim, H.K., Oh, H.Y. and Ryu, Y.U., 2010. A group recommendation system for online communities. International Journal of Information Management, 30(3), pp.212-219.
  8. Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734–749, June 2005.
  9. Contreras, D., Salamó, M. and Pascual, J., 2015. A Web-Based Environment to Support Online and Collaborative Group Recommendation Scenarios. Applied Artificial Intelligence, 29(5), pp.480-499.
  10. Christensen, I., Schiaffino, S. and Armentano, M., 2016. Social group recommendation in the tourism domain. Journal of Intelligent Information Systems, pp.1-23.
Index Terms

Computer Science
Information Sciences

Keywords

Recommendation System Group Purchase Location Sensitive Service.