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A New Improved Clustering Algorithm based Diversified Web Page Recommendation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 140 - Number 13 |
| Year of Publication: 2016 |
| Authors: Meghna Guru, S. Anitha Angayarkanni, J.C. Kavitha |
10.5120/ijca2016909542
|
Meghna Guru, S. Anitha Angayarkanni, J.C. Kavitha . A New Improved Clustering Algorithm based Diversified Web Page Recommendation. International Journal of Computer Applications. 140, 13 ( April 2016), 17-22. DOI=10.5120/ijca2016909542
Abstract
The tremendous growth of internet over the years, has given rise to the large number of web services, containing lot of information. Due to this information overload, it has become difficult to get the correct information. Web Service Recommendation system focuses on satisfying the user’s potential interests. Most of the existing recommendation approaches focus only on missing QoS values only, assuming that the result contains independent web services, which might not be true. As a result redundant web services appear in the list. The existing system takes into consideration active user’s QoS preferences as well as diversification of the web services list. First, the active user’s usage history is mined, and then the experiences of other service users are collected through collaborative filtering approach. Scores are computed for the web service candidates by measuring their relevance with historical and potential user interest and the QoS utility. Web Service graph is constructed based on the functional similarity of the web service candidates. Finally, the diversity-aware web service ranking algorithm is applied on the web service candidates based on the scores calculated and the diversified degree derived from the web service graph.
References
- Guosheng Kang, Mingdong Tang, Jianxun Liu, Xiaoqing Liu and Buqing Cao, “Diversifying Web Service Recommendation Results via Exploring Service Usage History,” IEEE Transactions on Service Computing, 2015.
- L. Yao, Q. Z. Sheng, A. Segev, and Yu, “Recommending Web Services via Combining Collaborative Filtering with Content-based Features,” Proc. of Int. Conf. on Web Services, pp. 42-49, 2013.
- M. Tang, Y. Jiang, J. Liu, and X. Liu, “Location-Aware Collaborative Filtering for QoS-based Service Recommendation,” Proc. of Int. Conf. on Web Services, pp. 202-209, 2012.
- J. Wu, L. Chen, Z. Zheng, M. R. Lyu, and Z. Wu, “Clustering Web Services to Facilitate Service Discovery,” Knowledge and Information Systems, pp. 1-23, 2012.
- W. Lin, W. Dou, X. Luo, and J. Chen, “A History Record-Based Service Optimization Method for QoS-Aware Service Composition,” Proc. of Int. Conf. on Web Services, pp. 666-673, 2011.
- S. S. Yau, and Y. Yin, “QoS-Based Service Ranking and Selection for Service-Based Systems,” Proc. of Int. Conf. on Service Computing, pp. 56-63, 2011.
- Y. Jiang, J. Liu, M. Tang, and X. Liu, “An Effective Web Service Recommendation Based on Personalized Collaborative Filtering,” Proc. of Int. Conf. on Web Services, pp. 211-218, 2011.
- X. Chen, X. Liu, Z. Huang, and H. Sun, “RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation,” Proc. of Int. Conf. on Web Services, pp. 9-16, 2010.
- Z. Zheng, H. Ma, M. R. Lyu, I. King, "Wsrec: a collaborative filtering based web service recommender system,” Proc. of Int. Conf. on Web Services, pp. 437-444, 2009.
- Mohammad Alrifai, Dimitrios Skoutas, Thomas Risse, “Selecting Skyline Services for QoS based Web Service Composition”, WWW2010, April 2630, 2010, Raleigh, North Carolina.
- Jian Wu, Liang Chen, Zibin Zheng, Michael R. Lyu, Zhaohui Wu, “Clustering Web services to facilitate service discovery,” © Springer-Verlag London 2013, Knowl Inf Syst (2014) 38:207–229.
- Yutu Liu, Anne H.H. Ngu, Liangzhao Zeng, “QoS Computation and Policing in Dynamic Web Service Selection”, WWW2004, May 17–22, 2004, New York, New York, USA. ACM 1-58113-912-8/04/0005.
- Lina Yao and Quan Z. Sheng, Aviv Segev, Jian Yu, “Recommending Web Services via Combining Collaborative Filtering with Content-based Features”, 2013 IEEE 20th International Conference on Web Services, 978-0-7695-5025-1/13 © 2013 IEEE.
- Lu Qin, Jeffrey Xu Yu, Lijun Chang, “Diversifying TopK Results”, 38th International Conference on Very Large Data Bases, Proceedings of the VLDB Endowment, Vol. 5, No. 11 © 2012 VLDB Endowment.
- Rong-Hua Li, Jeffrey Xu Yu, “Scalable Diversified Ranking on Large Graphs”, 2011 11th IEEE International Conference on Data Mining, © 2011 IEEE.
- Karen Spärck Jones, “A statistical interpretation of term specificity and its application in retrieval”, Journal of Documentation, Volume 60 Number 5 2004 pp. 493-502 Copyright © MCB University Press ISSN 0022-0418.
- Yechun Jiang, Jianxun Liu, Mingdong Tang, Xiaoqing (Frank) Liu, “An Effective Web Service Recommendation Method based on Personalized Collaborative Filtering”, 2011 IEEE International Conference on Web Services, 978-0-7695-4463-2/11 © 2011 IEEE.
- Mingdong Tang, Yechun Jiang, Jianxun Liu, Xiaoqing (Frank) Liu, “Location-Aware Collaborative Filtering for QoS-Based Service Recommendation”, 2012 IEEE 19th International Conference on Web Services, 978-0-7695-4752-7/12 © 2012 IEEE.
- Xi Chen, Xudong Liu, Zicheng Huang, and Hailong Sun, “Region KNN: A Sclable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation”, 2010 IEEE International Conference on Web Services, 978-0-7695-4128-0/10 © 2010 IEEE.
- Neil Hurley, Mi Zhang, “Novelty and Diversity in Top-N Recommendation – Analysis and Evaluation”, ACM Transactions on Internet Technology, Vol. 10, No. 4, Article 14, Publication date: March 2011.
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