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.
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References
Ahu Sieg,Bamshad Mobasher,Robin Burke: Improving the effectiveness of collaborative recommendation with ontology-based user profiles. Proceedings of the 1st International Workshop
on Information Heterogeneity and Fusion in Recommender Systems, pages 39-46, 2010-09-26.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Barry Smith,Case Based Recommendation, Springer Berlin Heidelberg,Pages 342-376, 2007.
Francesco Ricci,Fabio Del Missier: Personalized Product Recommendation through Interactive Query Management and Case-Based Reasoning, Springer-Verlag Berlin, Heidelberg, Pages 479-493, 2003.
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.
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.
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Subbulakshmi, S., Ramar, K., Renjitha, R., Sreedevi, T.U. (2016). Implementation of Adaptive Framework and WS Ontology for Improving QoS in Recommendation of WS. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_30
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DOI: https://doi.org/10.1007/978-3-319-47952-1_30
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