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Bi-CSem: A Semantically Inclined Bi-Classification Framework for Web Service Recommendation

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Innovations in Bio-Inspired Computing and Applications (IBICA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 649))

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Abstract

Web services are products in the era of service-oriented computing and cloud computing. As the number of web services on the Internet grows, selecting and recommending them becomes more important. Consequently, in the realm of service computing, how to propose the finest Web services for researchers is now a popular research topic. To determine the proper recommendation, the BI-CSem model was proposed and tested with multiple baseline models using real-world Web Service datasets in this research. Aside from that, thesaurus is built using Web service keywords gathered from Web service repositories such as UDDI,WSDI, and from the World Wide Web Cloud. The extracted terms are then subjected to semantic similarity, which is determined using SemantoSim, Concept similarity, and KL divergence measure, and the terms from the user, such as query, user click, and previous historical data, are pre-processed the terms from the semantic alignment are then classified using XGBoost, while the Web service dataset is classified using XGBoost and GRU. Semantic similarity is determined using just SemantoSim, based on the classification intersection of the top 75 percent words from the extracted terms from the two classifiers and features from the created intermediate term tree generated using STM. Finally, terms are reranked and recommended to the user, and the precision, accuracy, recall, F-measure, and FDR for the Web service recommendation system are calculated, and the Bi-CSem model is found to have an excellent precision percentage of 94.37% and the lowest FDR of 0.06.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    Deepak Surya & S. Palvannan

  2. Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

    Gerard Deepak

Authors
  1. Deepak Surya
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  2. S. Palvannan
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  3. Gerard Deepak
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Corresponding author

Correspondence to Gerard Deepak .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, Flame University, Pune, Maharashtra, India

    Ajith Abraham

  2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Interdisciplinary Studies Research Center (ISRC), Institute of Engineering, Polytechnique of Porto (ISEP/P.PORTO), INOV (Institute for Systems and Computer Engineering, Technology and Science), Porto, Portugal

    Ana Maria Madureira

  5. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Türkiye

    Cengiz Kahraman

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Surya, D., Palvannan, S., Deepak, G. (2023). Bi-CSem: A Semantically Inclined Bi-Classification Framework for Web Service Recommendation. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_40

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