Abstract
With growing momentousness of Internet applications, digital world is overwhelmed with huge number of web services. To ease the job of selecting relevant WS in service composition process, recommendation system of Web Services is designed. It uses semantic analysis of WS along with enhanced collaborative filtering. Ontology based Semantic Analysis performed using Tversky Content Similarity Measure helps to identify most similar functionally relevant WS. The collaborative filtering process uses DBSCAN clustering and PCC similarity to identify highly collaborative WS, based on ratings given by experienced users. To overcome the existence of sparse data in WS ratings and to enhance filtering process, SVM Regression is implemented before collaborative filtering. Relative frequency method is applied to amalgamate collaborative and sematic similarity values of WS. The methodology is proved to produce more realistic, accurate and efficient WS recommendation. Future focus may be towards knowledge based filtering with real world contextual information.
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References
Yao, L., Sheng, Q.Z., Ngu, A.H.H., Yu, J., Segev, A.: Unified collaborative and content-based web service recommendation. IEEE Trans. Serv. Comput. X(X) (2014)
Song, X., Zhou, T., Zhang, H.: Support vector regression estimation based on non-uniform lost function. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, 2005, pp. 1127–1130 (2005)
Yang, H., Fu, P., Yin, B., Ma, M., Tang, Y.: A semantic similarity measure between web services based on google distance. In: 35th IEEE Annual Computer Software and Applications Conference, pp. 14–19 (2011)
Sirin, E., Hendler, J., Parsia, B.: Filtering and selecting semantic web services with interactive composition techniques. IEEE Intell. Syst. 19, 42–49 (2004). doi:10.1109/MIS.2004.27
Subbulakshmi, S., Ramar, K., Renjitha, R., Sreedevi, T.U.: Implementation of adaptive framework and WS ontology for improving QoS in recommendation of WS. In: Rodriguez, J.C., Mitra, S., Thampi, S., El-Alfy, E.S. (eds.) Intelligent Systems Technologies and Applications 2016. ISTA 2016. AISC, vol. 530, pp. 383–396. Springer, Cham (2016). doi:10.1007/978-3-319-47952-1_30
Choi, C., Cho, M., Kang, E., Kim, P.: Travel ontology for recommendation system based on semantic web. In: Conference: Advanced Communication Technology, ICACT March 2006, pp. 624–627 (20006)
Gan, M., Dou, X., Jiang, R.: From ontology to semantic similarity: calculation of ontology-based semantic similarity. Sci World J 2013, Article ID 793091 (2013)
Hu, Y., Peng, Q., Hu, X., Yang, R.: Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering. IEEE Trans. Serv. Comput. 8(5), 782–794 (2015)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content based information in recommendation. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 714–720 (1998)
Smita, A.: Recommender system review. Int. J. Comput. Appl. 71(24), 0975–8887 (2013)
Bahramy, F., Crone, S.F.: Forecasting foreign exchange rates using support vector regression. An empirical evaluation of mean reversion using bollinger bands. In: IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pp. 34–41 (2013)
Yao, L., Sheng, Q.Z., Segev, A., Yu, J.: Recommending web services via combining collaborative filtering with content-based features. In: IEEE 20th International Conference on Web Services, pp. 42–49 (2013)
Maximilien, E.M., Singh, M.P.: A framework and ontology for dynamic web services selection. IEEE Internet Comput. 8(5), 84–93 (2004)
Lecue, F.: Combining collaborative filtering and semantic content based approaches to recommend web services. In: Proceedings of the 4th IEEE International Conference on Semantic Computing (ICSC 2010), Pittsburgh, PA, USA (2010)
Linden, G., Smith, B., York, J.: Amazon.com recommendations item-to-item collaborative filtering, pp. 76–80. IEEE Computer Society, Washington, DC (2003)
Sajeev, G.P., Ramya, P.T.: Effective web personalization system based on time and semantic relatedness. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, pp. 1390–1396 (2016)
Gaurav, S., Jithendranath, Y., Adil, A., Yadav, S., Kasturi, B.: A study to assess and enhance educational specific search on web for school children. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015, pp. 260–263. Institute of Electrical and Electronics Engineers Inc. (2015)
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Subbulakshmi, S., Ramar, K., Shaji, A., Prakash, P. (2018). Web Service Recommendation Based on Semantic Analysis of Web Service Specification and Enhanced Collaborative Filtering. In: Thampi, S., Mitra, S., Mukhopadhyay, J., Li, KC., James, A., Berretti, S. (eds) Intelligent Systems Technologies and Applications. ISTA 2017. Advances in Intelligent Systems and Computing, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-319-68385-0_5
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DOI: https://doi.org/10.1007/978-3-319-68385-0_5
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