The week's pick
Reseach Article
Study of Recommendation System for Web Portals
by
Lokesh Sharma,
Ashok Kumar Agrawal
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 84 - Number 9 |
| Year of Publication: 2013 |
| Authors: Lokesh Sharma, Ashok Kumar Agrawal |
10.5120/14601-2846
|
Lokesh Sharma, Ashok Kumar Agrawal . Study of Recommendation System for Web Portals. International Journal of Computer Applications. 84, 9 ( December 2013), 1-6. DOI=10.5120/14601-2846
@article{
10.5120/14601-2846,
author = {
Lokesh Sharma,
Ashok Kumar Agrawal
},
title = { Study of Recommendation System for Web Portals },
journal = {
International Journal of Computer Applications
},
issue_date = { December 2013 },
volume = { 84 },
number = { 9 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = {
1-6
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume84/number9/14601-2846/
},
doi = { 10.5120/14601-2846 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:26.644407+05:30
%A Lokesh Sharma
%A Ashok Kumar Agrawal
%T Study of Recommendation System for Web Portals
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 9
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Recommendation system (RS) is one of the most advanced approach which is popular commercially and in Research community. Many of the Web portals are using Recommender system to increase their customers and providing them better recommendation for purchasing of products. It learns from the customer's behavior of purchasing, rating and commenting, then deciding the score by help of Recommender system. In this paper, introducing about Recommendation system and its various types with their corresponding technologies that are currently used in E-commerce web portals. Later explaining some of the well known portals using Recommender system and comparison in techniques. Paper conclude with the applications of recommendation system and how they are increasing customer's to E-commerce.
References
- Interactive Critiquing for Catalog Navigation in E-Commerce R. Burke. Artificial Intelligence Review 18(3--4):245--267 (2002)
- Burke, R. D. ; Hammond, K. J. ; Yound, B. C. , "The FindMe approach to assisted browsing," IEEE Expert , vol. 12, no. 4, pp. 32,40, Jul/Aug 1997
- Goldberg, D. , Nichols, D. , Oki, B. M. , Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Communicationosf the A CM,3 5(12) 61-70. Maes, P. , Guttman, R. H. , and Moukas, A. G. 1999. Agents that buy and sell. Communication of the ACM4, 2(3), 81-91.
- Resnick, P. , Iacovou, N. , Suchak, M. , Bergstrom, P. , and Riedl, J. GroupLensa: n open architecture for collaborative filtering of netnews. In CSCW9 4: Proceedings of the conference on Computer supported cooperative work, 175-186. New York: ACM Press.
- Resnick, P. , Varian, H. R. : Recommender Systems. Communications of the ACM 40(3), 56–58 (1997)
- Bhargava, H. K. ; Sridhar, S. ; Herrick, C. , "Beyond spreadsheets: tools for building decision support systems," Computer , vol. 32, no. 3, pp. 31,39, Mar 1999
- Hoff, P. D. , Raftery, A. E. , Handcock, M. S. : Latent space approaches to social network analysis. Journal of the American Statistical Association, vol. 97(460), pp. 1090-1098 (2002)
- Backstrom, L. , Huttenlocher, D. P. , Kleinberg, J. M. , Lan, X. : Group formation in large social networks:Membership, growth, and evolution. In KDD, pp. 44-54 (2006)
- L. A. Adamic, O. Buyukkokten, and E. Adar. A social network caught in the web. First Monday, 8(6), June 2003.
- Flake, G. W. , Lawrence, S. , Giles, C. L. , Coetzee, F. : Self-organization and identification of web communities. IEEE Computer, vol. 35(3), pp. 66-71 (2002)
- Book Section D 2008 978-3-540-88874-1 B On the Move to Meaningful Internet Systems: OTM 2008 Workshops 5333 Lecture Notes in Computer Science E Meersman, Robert E Tari, Zahir E Herrero, Pilar R 10. 1007/978-3-540-88875-8_41 T Group Recommendation System for Facebook
- Davidson, James, et al. "The YouTube video recommendation system. " Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.
- Zhou, Renjie, Samamon Khemmarat, and Lixin Gao. "The impact of YouTube recommendation system on video views. " Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, 2010.
- Anup Prakash Warade, Vignesh Murali Natarajan, Siddharth Sharad Chandak: cognizant 20-20 insights [15Ahmad Wasfi, Ahmad M. "Collecting user access patterns for building user profiles and collaborative filtering. " Proceedings of the 4th international conference on Intelligent user interfaces. ACM, 1998.
- Balabanovi?, Marko, and Yoav Shoham. "Fab: content-based, collaborative recommendation. " Communications of the ACM 40. 3 (1997): 66-72.
- Claypool, Mark, et al. "Combining content-based and collaborative filters in an online newspaper. " Proceedings of ACM SIGIR workshop on recommender systems. Vol. 60. 1999.
- Resnick, Paul, and Hal R. Varian. "Recommender systems. " Communications of the ACM 40. 3 (1997): 56-58.
- Herlocker, Jonathan L. , et al. "Evaluating collaborative filtering recommender systems. " ACM Transactions on Information Systems (TOIS) 22. 1 (2004): 5-53.
- Huang, Zan, et al. "A graph-based recommender system for digital library. " Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries. ACM, 2002.
- Condliff, Michelle Keim, et al. "Bayesian mixed-effects models for recommender systems. " Proc. ACM SIGIR. Vol. 99. 1999.
- Levi, Asher, et al. "Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. " Proceedings of the sixth ACM conference on Recommender systems. ACM, 2012.
- Adomavicius, Gediminas, and Alexander Tuzhilin. "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. " Knowledge and Data Engineering, IEEE Transactions on 17. 6 (2005): 734-749.
- Rashid, Al Mamunur, et al. "Getting to know you: learning new user preferences in recommender systems. " Proceedings of the 7th international conference on Intelligent user interfaces. ACM, 2002.
- Schein, Andrew I. , et al. "Methods and metrics for cold-start recommendations. " Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2002.
- N. M. T. Watch, "Online Travel Market," April 2011. Available: http://www. newmediatrendwatch. com/worldoverview/91-online-travel-market
- Levi, Asher, et al. "Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. " Proceedings of the sixth ACM conference on Recommender systems. ACM, 2012. [28 ] Bridge, Derek, et al. "Case-based recommender systems. " The Knowledge Engineering Review 20. 03 (2005): 315-320.
- Steiniger, Stefan, Moritz Neun, and Alistair Edwardes. "Foundations of location based services. " Lecture Notes on LBS 1 (2006).
- Turban E, Lee JK, King D, McKay J, Marshall P (2008) Electronic Commerce – A Managerial Perspective. Prentice Hall.
- [Tumas and Ricci, 2009] Personalized Mobile City Transport Advisory System. In Information and Communication Technologies in Tourism 2009, Pages: 173-?184, Springer
- [Burke, 2007] R. Burke. The adaptive web. chapter Hybrid web recommender systems, pages 377–408. Springer-Verlag, Berlin, Heidelberg, 2007.
- Ntoutsi, Irene, et al. "grecs: A group recommendation system based on user clustering. " Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2012.
Index Terms
Computer Science
Information Sciences
Keywords