Improving the performance of web service recommenders using semantic similarity


  • Juan Manuel Adán Coello Faculdade de Engenharia de Computação, Pontifícia Universidade Católica de Campinas (PUC-Campinas), Campinas, SP, Brasil
  • Carlos Miguel Tobar Faculdade de Engenharia de Computação, Pontifícia Universidade Católica de Campinas (PUC-Campinas), Campinas, SP, Brasil
  • Yang Yuming Faculdade de Engenharia de Computação, Pontifícia Universidade Católica de Campinas (PUC-Campinas), Campinas, SP, Brasil


Collaborative filtering, Recommender systems, Semantic similarity, Semantic Web Services, Sparse data


This paper addresses issues related to recommending Semantic Web Services (SWS) using collaborative filtering (CF). The focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms. Two CF algorithms are presented and discussed: a memory-based algorithm, using the k-NN method, and a model-based algorithm, using the k-means method. In both algorithms, similarity between users is computed using the Pearson Correlation Coefficient (PCC). One of the limitations of using the PCC in this context is that in those instances where users have not rated items in common it is not possible to compute their similarity. In addition, when the number of common items that were rated is low, the reliability of the computed similarity degree may also be low. To overcome these limitations, the presented algorithms compute the similarity between two users taking into account services that both users accessed and also semantically similar services. Likewise, to predict the rating for a not yet accessed target service, the algorithms consider the ratings that neighbor users assigned to the target service, as is normally the case, while also considering the ratings assigned to services that are semantically similar to the target service. The experiments described in the paper show that this approach has a significantly positive impact on prediction accuracy, particularly when the user-item matrix is sparse.


Download data is not yet available.


[1] Papazoglou, M. P., and Georgakopoulos, D. (2003). Service-Oriented Computing. Communications of the ACM, 46(10), 25–28.
[2] Pan, Y., Tang, Y., & Li, S. (2011). Web Services Discovery in a Pay-As-You-Go Fashion. Journal of Universal Computer Science, 17(14), 2029–2047.
[3] Christensen, E., Curbera, F., Meredith, G., and Weerawarana, S. (2001). Web Services Description Language (WSDL) 1.1, 2001. At http://www.w3. org/TR/2001/NOTE-wsdl-20010315. Retrieved from 2001/NOTE-wsdl-20010315
[4] McIlraith, S. A., Son, T. C., and Zeng, H. (2005). Semantic web services. Intelligent Systems, IEEE, 16(2), 46–53.
[5] Pedrinaci, C., and Domingue, J. (2010). Toward the Next Wave of Services: Linked Services for the Web of Data. Journal of Universal Computer Science, 16(13), 1694–1719.
[6] W3C OWL Working Group. (2012). OWL 2 Web Ontology Language Document Overview (Second Edition).
[7] Brickley, D., and Guha, R. V. (2006). RDF Vocabulary Description Language 1.0: RDF Schema, 2004. Retrieved from http://www.
[8] Tsetsos, V., Anagnostopoulos, C., and Hadjiefthymiades, S. (2006). On the Evaluation of Semantic Web Service Matchmaking Systems. Web Services, 2006. ECOWS’06. 4th European Conference on, 255–264.
[9] Sreenath, R. M., and Singh, M. P. (2004). Agent-based service selection. Web Semantics: Science, Services and Agents on the World Wide Web, 1(3), 261–279.
[10] Stein, S., Payne, T. R., & Jennings, N. R. (2009). Flexible provisioning of web service workflows. ACM Transactions on Internet Technology (TOIT), 9(1), 2
[11] Tizzo, N. P., Adán-Coello, J. M., & Cardozo, E. (2011). Automatic composition of semantic web services using A-Teams with genetic agents. In Evolutionary Computation (CEC), 2011 IEEE Congress on (pp. 370–377).
[12] Su, X., and Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, 2009, 1–19.
[13] Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Systems, 69, 175–186.
[14] Zheng, Z., Ma, H., Lyu, M. R., & King, I. (2011). QoS-aware Web service recommendation by collaborative filtering. Services Computing, IEEE Transactions On, 4(2), 140–152.
[15] Van Moorsel, A. (2001). Metrics for the Internet Age: Quality of Experience and Quality of Business. Fifth International Workshop on Performability Modeling of Computer and Communication Systems, Arbeitsberichte des Instituts f\ür Informatik, Universit\ät Erlangen-N\ürnberg, Germany (Vol. 34, pp. 26–31).
[16] Martin, D., Burstein, M., Hobbs, J., Lassila, O., McDermott, D., McIlraith, S., Narayanan, S., et al. (2004). OWL-S: Semantic Markup for Web Services. Retrieved from
[17] Klusch, M., Fries, B., and Sycara, K. (2009). OWLS-MX: A Hybrid Semantic Web Service Matchmaker for OWL-S Services. Web Semantics: Science, Services and Agents on the World Wide Web, 7(2), 121–133. doi:10.1016/j.websem.2008.10.001
[18] Mobasher, B., Burke, R., and Sandvig, J. J. (2006). Model-Based Collaborative Filtering as a Defense Against Profile Injection Attacks. Proceedings of the National Conference on Artificial Intelligence (Vol.21, p. 1388).
[19] Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2002). Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. Data Mining and Knowledge Discovery, 6(1), 61–82.
[20] Rong, W., Liu, K., and Liang, L. (2009). Personalized Web Service Ranking via User Group Combining Association Rule. Proceedings of the 2009 IEEE International Conference on Web Services-Volume 00 (pp. 445–452).
[21] Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5–53.
[22] Chen, X., Liu, X., Huang, Z., and Sun, H. (2010). RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation. 2010 IEEE International Conference on Web Services (pp. 9–16). Presented at the 2010 IEEE International Conference on Web Services (ICWS), Miami, FL, USA. doi:10.1109/ICWS.2010.27
[23] Wu, J., Chen, L., Feng, Y., Zheng, Z., Zhou, M. C., & Wu, Z. (2013). Predicting quality of service for selection by neighborhood-based collaborative filtering. Systems, Man, and Cybernetics: Systems, IEEE Transactions On, 43(2), 428–439.
[24] Blake, M. B., & Nowlan, M. F. (2007). A web service recommender system using enhanced syntactical matching. In Web Services, 2007. ICWS 2007. IEEE International Conference on (pp. 575–582).
[25] Averbakh, A., Krause, D., & Skoutas, D. (2009). Exploiting User Feedback to Improve Semantic Web Service Discovery. Presented at the 8th International Semantic Web Conference (ISWC 2009).
[26] McLaughlin, M. R., and Herlocker, J. L. (2004). A collaborative filtering algorithm and evaluation metric that accurately model the user experience. Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (pp.329–336).




How to Cite

Adán Coello, J. M., Tobar, C. M., & Yuming, Y. (2014). Improving the performance of web service recommenders using semantic similarity. Journal of Computer Science and Technology, 14(02), p. 80–87. Retrieved from



Original Articles