Automated Analytical Model for Content Based Selection of Web Services

  • S. Subbulakshmi
  • K. Ramar
  • Aparna OmanakuttanEmail author
  • Arya Sasidharan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


There are various inbound web services which prescribe services to clients. Specialists are more engaged in making framework for proposal of web service (WS) which limit the intricacy of selection process and improve the quality of service (QOS) suggestion. Our work implements a framework which recommends web services using an analytical model based on the contextual information provided by the service providers. This system helps users obtain high quality service automatically. Adaptive work performs feature reduction, similarity and ranking of WS. The important feature reduction process helps identify attribute values with maximum accuracy which results in proper evaluation of data. Efficient selection of WS for service composition requires better methods which properly calculate the similar values. A similarity helps to identify the closest services as per the requirement in the process of service composition. Ultimately, the system automatically selects the set of web services with highest similarity scores from the optimized set of web service description.


SVD Analytical model Content based Recommendation Feature reduction 


  1. 1.
    Yao, L., Sheng, Q.Z., Ngu, A.H., Yu, J., Segev, A.: Unified collaborative and content based webservice recommendation. IEEE Trans. Serv. Comput. 8, 453–466 (2014)CrossRefGoogle Scholar
  2. 2.
    Yu, L., Gao, M., Xiao, X., Li, X., Xiong, Q.: Important user group based web service recommendation. In: 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 413–418 (2017)Google Scholar
  3. 3.
    Subbulakshmi, S., Ramar, K., Renjitha, R., Sreedevi, T.U.: Implementation of adaptive framework and WS ontology for improving QoS in recommendation of WS. Intelligent Systems Technologies and Applications 2016. AISC, vol. 530, pp. 383–396. Springer, Cham (2016). Scholar
  4. 4.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: 10th International Conference on World Wide Web, pp. 285–295 (2001)Google Scholar
  5. 5.
    Vadivelou, G.: Collaborative filtering based web service recommender system using users. Satisfaction on QoS attributes. In: International Conference, Inventive Computation Technologies (ICICT), vol. 3, pp. 1–5 (2016)Google Scholar
  6. 6.
    Linden, G., Smith, B., York, J.: Recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7, 76–80 (2003)CrossRefGoogle Scholar
  7. 7.
    Gupta, J., Gadge, J.: Performance analysis of recommendation system based on collaborative filtering and demorgraphics . In: International Conference on Communication, Information & Computing Technology (ICCICT), pp. 1–6 (2015)Google Scholar
  8. 8.
    Gudla, S.K., Bose, J., Gajam, V., Srinivasa, S.: Relevancy ranking of user recommendations of services based on browsing patterns. In: 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 765–768 (2017)Google Scholar
  9. 9.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4, 140–152 (2010)CrossRefGoogle Scholar
  10. 10.
    Devika, P., Jisha, R.C., Sajeev, G.P.: A novel approach for book recommendation systems In: 2016 IEEE International Conference on Computational Intelligence and Computing Research, (ICCIC), pp. 1–6 (2016)Google Scholar
  11. 11.
    Arunachalam, N., Amuthan, A., Sharmilla, M., Ushanandhini, K.: Survey on web service recommendation based on user history. In: International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), pp. 305–309 (2017)Google Scholar
  12. 12.
    Phillips, R.D., Watson, L.T., Wynne, R.H., Blinn, C.E.: Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier. ISPRS J. Photogrammetry Remote Sens. 64, 107–116 (2009)CrossRefGoogle Scholar
  13. 13.
    Reshma, R., Sowmya, V., Soman, K.P.: Effect of Legendre-Fencheldenoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification. Neural Comput. Appl. 29, 301–310 (2018)CrossRefGoogle Scholar
  14. 14.
    Hendrix, W., Palsetia, D., Patwary, M.M.A., Agrawal, A., Liao, W.K., Choudhary, A.: A scalable algorithm for single-linkage hierarchical clustering on distributed-memory architectures. In: IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), pp. 7–13 (2013)Google Scholar
  15. 15.
    Sabarish, B.A., Karthi, R., Gireeshkumar, T.: Clustering of trajectory data using hierarchical approaches. In: Computational Vision and Bio Inspired Computing, pp. 215–226 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • S. Subbulakshmi
    • 1
  • K. Ramar
    • 2
  • Aparna Omanakuttan
    • 1
    Email author
  • Arya Sasidharan
    • 1
  1. 1.Department of Computer Science and ApplicationAmrita Vishwa VidyapeethamAmritapuriIndia
  2. 2.Department of Computer Science and EngineeringEinstein College of EngineeringTirunelveliIndia

Personalised recommendations