Abstract
With the rapid growth of Web Services in the past decade, the issue of QoS-aware Web service recommendation is becoming more and more critical. Web service QoS is highly relevant to the corresponding invocation context like invocation time and location. Therefore, it is of paramount importance to collect the QoS data with different invocation context. We have crawled over 30,000 Web services distributed across Internet. In this work, we propose to use crowdsourcing to collect the required QoS data. This is achieved through two approaches. On the one hand, we deploy a generic Web service invocation client to 343 Planet-Lab nodes and these nodes serve as simulated users distributing worldwide. The Web service invocation client is scheduled to invoke target Web services from time to time. On the other hand, we design and develop a mobile crowdsourced Web service tesing framework on Android platform, with which a user can easily invoke selected Web services. With the above two approaches, the observed service invocation data, e.g. response time, will be collected in this way. Then we design a Temporal QoS-Aware Web Service Recommendation Framework to predict missing QoS value under various temporal context. Further, we formalize this problem as a generalized tensor factorization model and propose a Non-negative Tensor Factorization (NTF) algorithm which is able to deal with the triadic relations of user-service-time model. Extensive experiments are conducted based on collected Crowdsourced testing data. The comprehensive experimental analysis shows that our approach achieves better prediction accuracy than other approaches.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bader B.W., Kolda T.G., et al. (2012) Matlab tensor toolbox version 2.5. http://www.sandia.gov/tgkolda/TensorToolbox/
Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In:ICML, vol. 98, pp. 46–54 (1998)
Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized Qos-aware web service recommendation and visualization. IEEE Trans. Serv. Comput. 6(1), 35–47 (2013)
Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the world-wide web. Commun. ACM 54(4), 86–96 (2011)
Dong X., Halevy A.Y., Madhavan J., Nemes E., Zhang J.: Simlarity search for web services. In: VLDB, pp. 372–383 (2004)
He, Q., Yan, J., Yang, Y., Kowalczyk, R., Jin, H.: A decentralized service discovery approach on peer-to-peer networks. IEEE Trans. Serv. Comput, 6(1), 64–75 (2013)
Li C., Zhang R., Huai J., Guo X., Sun H.: A probabilistic approach for web service discovery. In: IEEE SCC, pp. 49–56 (2013)
Paliwal, A.V., Shafiq, B., Vaidya, J., Xiong, H., Adam, N.R.: Semantics-based automated service discovery. IEEE Trans. Serv. Comput. 5(2), 260–275 (2012)
Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: state of the art and research challenges. IEEE Comput. 40(11), 38–45 (2007)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup. workshop, vol. 2007 pp. 5–8 (2007)
Sarwar B., Karypis G., Konstan J., Riedl J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)
Segev, A., Toch, E.: Context-based matching and ranking of web services for composition. IEEE Trans. Serv. Comput. 2(3), 210–222 (2009)
Seung, D., Lee, L.: Algorithms for non-negative matrix factorization. Adv. Neural.Inf. Process. Syst. 13, 556–562 (2001)
Shao L., Zhang J., Wei Y., Zhao J., Xie B., Mei H.: Personalized Qos prediction for web services via collaborative filtering. In: IEEE International Conference on IEEE Web Services, ICWS 2007, pp. 439–446 (2007)
Shashua A., Hazan T.: Non-negative tensor factorization with applications to statistics and computer vision. In: Proceedings of the 22nd International Conference on Machine learning, pp. 792–799.ACM (2005)
Sun H., Wang X., Yan M., Tang Y., Liu X.: Towards a scalable paaS for service oriented software. In: ICPADS, pp. 522–527 (2013)
W3C Web services activity. http://www.w3.org/2002/ws/ (2002)
Yan M., Sun H., Wang X., Liu X.: WS-TaaS: a testing as a service platform for web service load testing. In: ICPADS, pp. 456–463 (2012)
Zhang W., Sun H., Liu X., Guo X.: Temporal Qos-aware web service recommendation via non-negative tensor factorization. In: WWW, pp. 585–596 (2014)
Zheng, G., Bouguettaya, A.: Service mining on the web. IEEE Trans. Serv. Comput. 2(1), 65–78 (2009)
Zheng Z., Ma H., Lyu M.R., King I.: WSRec: a collaborative filtering based web service recommender system. In: IEEE International Conference on IEEE Web Services ICWS 2009, pp. 437–444 (2009)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: Qos-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)
Zheng Z., Ma H., Lyu M., King I.: Collaborative web service Qos prediction via neighborhood integrated matrix factorization (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sun, H., Zhang, W., Yan, M., Liu, X. (2015). Recommending Web Services Using Crowdsourced Testing Data. In: Li, W., Huhns, M., Tsai, WT., Wu, W. (eds) Crowdsourcing. Progress in IS. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47011-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-662-47011-4_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47010-7
Online ISBN: 978-3-662-47011-4
eBook Packages: Business and EconomicsBusiness and Management (R0)