International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Location- Aware QoS based Web Service Recommendation and Ranking

Author : Chinnu Priya J V ¹ Suja Rani M S ²

Date of Publication :7th February 2016

Abstract: The number of web services with similar functionality increases which makes the service users depend on web service recommendation systems. During these days it was found that the service users pay more attention on the non-functional properties which are also known as Quality Of Service (QoS) in selecting a best Web Service. Collaborative filtering methods are used in predicting the QoS values effectively. Existing methods generally consider a single QoS factor in recommendation. They rarely consider the personalized influence of users and services in determining the similarity between users and services. The proposed system is improved by integrating different QoS aspects in consideration which includes response time, CPU usage, latency.etc. By including more QoS values helps in finding the best web service for the service user and this is done by replacing the Pearson Correlation Coefficient with Cosine Similarity on finding the similarity computation. The proposed system is a ranking based system which integrates user-based and item-based QoS predictions thereby providing a hybrid approach. Many of the nonfunctional properties related to web services depends on user and the service location. The system thus consider the user and the service location to find the similar neighbours for the target user and service and thereby providing a personalized web service recommendation for the service users.

Reference :

1. S. S. Yau, Y. Yin,"QoS-based service ranking and selection for service based systems”, in Proc. of the International conference on Services Computing, Washington DC, USA, July, 2011, pp. 56 - 63.
2. B. Sarwar, G. Kari’s, J. Konstanz, and J. Riedl, "ItemBased Collaborative Filtering Recommendation Algorithms", in Proc.10th Int’l Conf. World Wide Web, 2001, pp. 285-295.
3. Zheng, H. Ma, M. R. Lyu, and I. King "QoS-Aware Web Service Recommendation by Collaborative Filtering", IEEE Trans. on Services Computing, 2011, vol.4, no.2, pp.140-152.
4. L. Shao, J. Zhang, Y. Wei, J. Zhao, B. Xie, and H. Mei,"Personalized QoS prediction for Web services via collaborative filtering", in Proc. 5th International Conference on Web Services, 2007, pp. 439-446.
5. M. Tang,Y. Jiang, J. Liu,X. F. Liu: "Location-Aware Collaborative Filtering for QoS-Based Service Recommendation", in Proc. 10th International Conference on Web Services, Hawaii, USA, June 2012, pp.202-209.
6. G. Adomavicius, and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions", IEEE Trans. Knowledge and Data Engineering, 2005, pp.734 –749.
7. M. Alrifai, and T. Rise, "Combining Global Optimization with Local Selection for Efficient QoSaware Service Composition", in Proc. of the International World Wide Web Conference, Apr. 2009, pp. 881-890.
8. J. Wu, L. Chen, Y. Feng,Z. Zheng, M. Zhou, and Z. Wu, "Predicting QoS for Service Selection by Neighborhood-based Collaborative Filtering",IEEE Trans. on System, Man, and Cybernetics, Part A, 2013, vol. 43,no. 2, pp. 428-439
9. K. Elgazzar, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems", IEEE Computer, vol. 42, no. 8, pp. 30-37, 2009
10. L. Shao, J. Zhang, Y. Wei, J. Zhao, B. Xie, and H. Mei,"Personalized QoS prediction for Web services via collaborative filtering", in Proc. 5th International Conference on Web Services, 2007, pp. 439-446.
1. S. S. Yau, Y. Yin,"QoS-based service ranking and selection for service based systems”, in Proc. of the International conference on Services Computing, Washington DC, USA, July, 2011, pp. 56 - 63
2. B. Sarwar, G. Kari’s, J. Konstanz, and J. Riedl, "ItemBased Collaborative Filtering Recommendation Algorithms", in Proc.10th Int’l Conf. World Wide Web, 2001, pp. 285-295.
3. Zheng, H. Ma, M. R. Lyu, and I. King "QoS-Aware Web Service Recommendation by Collaborative Filtering", IEEE Trans. on Services Computing, 2011, vol.4, no.2, pp.140-152.
4. L. Shao, J. Zhang, Y. Wei, J. Zhao, B. Xie, and H. Mei,"Personalized QoS prediction for Web services via collaborative filtering", in Proc. 5th International Conference on Web Services, 2007, pp. 439-446.
5. M. Tang,Y. Jiang, J. Liu,X. F. Liu: "Location-Aware Collaborative Filtering for QoS-Based Service Recommendation", in Proc. 10th International Conference on Web Services, Hawaii, USA, June 2012, pp.202-209.
6. G. Adomavicius, and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions", IEEE Trans. Knowledge and Data Engineering, 2005, pp.734 –749
7. G. Adomavicius, and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions", IEEE Trans. Knowledge and Data Engineering, 2005, pp.734 –749
8. J. Wu, L. Chen, Y. Feng,Z. Zheng, M. Zhou, and Z. Wu, "Predicting QoS for Service Selection by Neighborhood-based Collaborative Filtering",IEEE Trans. on System, Man, and Cybernetics, Part A, 2013, vol. 43,no. 2, pp. 428-439.
9. K. Elgazzar, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems", IEEE Computer, vol. 42, no. 8, pp. 30-37, 2009.
10. L. Shao, J. Zhang, Y. Wei, J. Zhao, B. Xie, and H. Mei,"Personalized QoS prediction for Web services via collaborative filtering", in Proc. 5th International Conference on Web Services, 2007, pp. 439-446.