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Implementation of Adaptive Framework and WS Ontology for Improving QoS in Recommendation of WS

  • S. Subbulakshmi
  • K. Ramar
  • R. Renjitha
  • T. U. Sreedevi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 530)

Abstract

With the advent of more users accessing internet for information retrieval, researchers are more focused in creating system for recommendation of web service(WS) which minimize the complexity of selection process and optimize the quality of recommendation. This paper implements a framework for recommendation of personalized WS coupled with the quality optimization, using the quality features available in WS Ontology. It helps users to acquire the best recommendation by consuming the contextual information and the quality of WS. Adaptive framework performs i) the retrieval of context information ii) calculation of similarity between users preferences and WS features, similarity between preferred WS with other WS specifications iii) collaboration of web service ratings provided by current user and other users. Finally, WS quality features are considered for computing the Quality of Service. The turnout of recommendation reveals the selection of highly reliable web services, as credibility is used for QoS predication.

Keywords

web services quality factors context information ontology optimization of QoS for WS 

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References

  1. 1.
    Ahu Sieg,Bamshad Mobasher,Robin Burke: Improving the effectiveness of collaborative recommendation with ontology-based user profiles. Proceedings of the 1st International WorkshopGoogle Scholar
  2. 2.
    on Information Heterogeneity and Fusion in Recommender Systems, pages 39-46, 2010-09-26.Google Scholar
  3. 3.
    Deng-Neng CHEN, Yao-Chun CHIANG: Combining Personal Ontology and Collaborative Filtering to Design a Document Recommendation System. Journal of Service Science and Management, Vol.2, pages 322-328, 2013.Google Scholar
  4. 4.
    Divya Sachan,Saurabh Kumar Dixit,and Sandeep Kumar: QoS AWARE FORMALIZED MODEL FOR SEMANTIC WEB SERVICE SELECTION. International Journal ofWeb and Semantic Technology (IJWesT) Vol.5, No.4, October 2014.Google Scholar
  5. 5.
    E. Michael Maximilien, Munindar P. Singh: A Framework and Ontology for Dynamic Web Services Selection. IEEE Internet Computing, Volume:8, Issue:5, Sept-Oct 2004.Google Scholar
  6. 6.
    Jyoti Gupta: Performance Analysis of Recommendation System Based On Collaborative Filtering and Demographics. International Conference on Communication, Information and Computing Technology (ICCICT), Jan. 15-17, Page(s): 1-6, 2015.Google Scholar
  7. 7.
    Kyriakos Kritikos,Dimitris Plexousakis: Requirements for QoS-Based Web Service Description and Discovery. IEEE Transactions on Services Computing Vol.2, Issue: 4, 11 Sept. 2009.Google Scholar
  8. 8.
    Lina Yao and Quan Z. Sheng, Aviv Segev, Jian Yu: Recommending Web Services via Combining Collaborative Filtering with Content-based Features. IEEE 20th International Conference on Web Services, Page(s): 42-49, 2013.Google Scholar
  9. 9.
    Maringela Vanzin, Karin Becker, and Duncan Dubugras Alcoba Ruiz: Ontology-Based Filtering Mechanisms forWeb Usage Patterns Retrieval. 6th International Conference, EC-Web, Copenhagen, Denmark, August 23-26, pp. 267277, 2005.Google Scholar
  10. 10.
    Nitai B. Silva, Ing-Ren Tsang, George D.C. Cavalcanti, and Ing-Jyh Tsang: A Graph-Based Friend Recommendation System Using Genetic Algorithm. IEEE Congress on Evolutionary Computation (CEC), pages 18-23 July 2010.Google Scholar
  11. 11.
    Sunita Tiwari,Saroj Kaushik: A Non Functional Properties BasedWeb Service Recommender System. International Conference on Computational Intelligence and Software Engineering (CISE), Page(s): 1 - 4, 10-12 Dec. 2010.Google Scholar
  12. 12.
    Wang Chun Hong, Yun Cheng, P.R.China: Design and Development on Internet-based Information Filtering System. International Conference on Educational and Network Technology (ICENT), 25-27 June 2010.Google Scholar
  13. 13.
    Wang Denghui, Huang Hao, Xie Changsheng: A Novel Web Service Composition Recommendation Approach Based on Reliable QoS. IEEE Eighth International Conference on Networking, Architecture and Storage (NAS), Page(s): 321 - 325,17-19 July 2013.Google Scholar
  14. 14.
    Yaqiong Wang, Junjie Wu, Zhiang Wu, Hua Yuan: Popular Items or Niche Items: Flexible Recommendation using Cosine Patterns. IEEE International Conference on Data Mining Workshop (ICDMW), Page(s): 205 - 212, 14-14 Dec. 2014.Google Scholar
  15. 15.
    Xi Chen, Zibin Zheng, Qi Yu, and Michael R. Lyu: Web Service Recommendation via Exploiting Location and QoS Information. IEEE Transactions on Parallel and Distributed Systems, Page(s): 1913 - 1924, July 2014.Google Scholar
  16. 16.
    Greg Linden, Brent Smith, and Jeremy York: Amazon.com Recommendations Item-to-Item Collaborative Filtering. Published by IEEE Computer Society on Page(s): 76 - 80, Feb. 2003.Google Scholar
  17. 17.
    Barry Smith,Case Based Recommendation, Springer Berlin Heidelberg,Pages 342-376, 2007.Google Scholar
  18. 18.
    Francesco Ricci,Fabio Del Missier: Personalized Product Recommendation through Interactive Query Management and Case-Based Reasoning, Springer-Verlag Berlin, Heidelberg, Pages 479-493, 2003.Google Scholar
  19. 19.
    Yuanyuan Wang,Stephen Chi-fai Chanand Grace Ngai: Applicability of Demographic Recommender System to Tourist Attractions - A CaseStudy on TripAdvisor. ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Pages 97-101, 2012.Google Scholar
  20. 20.
    S. B. Kotsiantis, Zaharakis, Pintelas: Machine Learning: a review of classification and combining techniques, Springer Science+Business Media B.V. pp 159190, 10 November 2007.Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • S. Subbulakshmi
    • 1
  • K. Ramar
    • 2
  • R. Renjitha
    • 1
  • T. U. Sreedevi
    • 1
  1. 1.Department of Computer Science and ApplicationsAmrita School of Engineering, Amritapuri, Amrita Vishwa Vidhyapeetham, Amrita UniversityKollamIndia
  2. 2.Department of Computer Science and EngineeringEinstein College of EngineeringTirunelveliIndia

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