A Review Paper: Personalized QOS Web Service Recommendation and Visualization
Keywords:
service recommendation, collaboration filtering, visualization, QoSAbstract
Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender systems for experts, jokes, restaurants, financial services, life insurance, persons (online dating), and Twitter followers. In this paper, we present review of collaboration filtering for accurate web recommendation service using characteristics of QoS and user location and we use recommendation visualization map.
References
Xi chen,Zibin zheng ,Xudong Liu,Zicheng Huang and Hailong Sun, “Personalized Qos-Aware Web Service Recommendation and visualization”.
M.B. Blake and M.F. Nowlan, “A Web Service Recommender System Using Enhanced Syntactical Matching,” Proc. Int’l Conf. Web Services, pp. 575-582, 2007.
J.S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. 14th Conf. Uncertainty in Artificial Intelligence (UAI ’98), pp. 43-52, 1998.
S. Haykin, Neural Networks: A Comprehensive Foundation, seconded. Prentice-Hall, 1999.
J.L. Herlocker, J.A. Konstan, and J. Riedl, “Explaining Collaborative Filtering Recommendations,” Proc. ACM Conf. Computer Supported Cooperative Work, pp. 241-250, 2000.
J. Himberg, “A SOM Based Cluster Visualization and Its Application for False Coloring,” Proc. IEEE-INNS-ENNS Int’l Joint Conf. Neural Networks, pp. 587-592, 2000, vol. 3, doi:10.1109/ IJCNN.2000.861379.
Hsu, and S.K. Halgamuge, “Class Structure Visualization with Semi-Supervised Growing Self-Organizing Maps,” Neurocomputing, vol. 71, pp. 3124-3130, 2008.
T. Kohonen, “The Self-Organizing Map,” Proc. IEEE, vol. 78, no. 9, pp. 1464-1480, Sept. 1990.
S. Kaski, J. Venna, and T. Kohonen, “Coloring that Reveals High- Dimensional Structures in Data,” Proc. Sixth Int’l Conf. Neural Information Processing, vol. 2, pp. 729-734, 1999.
;J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordan, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Comm. ACM, vol. 40, no. 3, pp. 77-87, 1997.
G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, Jan./Feb. 2003.
Z. Maamar, S.K. Mostefaoui, and Q.H. Mahmoud, “Context for Personalized Web Services,” Proc. 38th Ann. Hawaii Int’l Conf., pp. 166b-166b, 2005.
M.R. McLaughlin and J.L. Herlocker, “A Collaborative Filtering Algorithm and Evaluation Metric That Accurately Model the User Experience,” Proc. Ann. Int’l ACM SIGIR Conf., pp. 329-336, 2004.
B. Mehta, C. Niederee, A. Stewart, C. Muscogiuri, and E.J.Neuhold, “An Architecture for Recommendation Based Service Mediation,” Semantics of a Networked World, vol. 3226, pp. 250-262,2004.
J. Zhang, H. Shi, Y. Zhang, “Self-Organizing Map Methodology and Google Maps Services for Geographical Epidemiology Mapping,” Proc. Digital Image Computing: Techniques and Applications, pp. 229-235, 2009, doi:10.1109/DICTA.2009.46.
G.shoba, A.delphie, K.lakshmi, A.rajeswari “Services recommendation accuracy and interactive Visualization from personalized QoS”International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Volume-2,Issue-2,Feb.-2014
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
