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WCMIVR: A Web 3.0 Compliant Machine Intelligence Driven Scheme for Video 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))

Abstract

Video recommendation is the need of the hour in the era of Web 3.0 . And there is a need for semantically inclined Web 3.0 compliant framework for video recommendation. In this paper, a WCMIVR framework which is a Web 3.0 compliant web video recommendation framework. WCMIVR has been proposed, although it is a query driven approach, it is centred on staged query enrichment by auxiliary knowledge addition. The proposed WCMIVR model is a knowledge centric semantically inclined framework where the TF-IDF is applied on the documents crawled from the web repository in order to aggregate the enriched categories of videos and also informative words are added from the TF-IDF model. Wikidata and Google Knowledge graph API are standard knowledge stores which are used further used for enrichment of the video categories. Ontology alignment is applied for feature discovery in order to classify the dataset of videos using Machine learning based logistic regression feature controlled classifier. The semantic similarity is calculated using Horn’s index, Twitter semantic similarity and Jaccard similarity which improvisions a strong relevant computation mechanism in order to rank and recommend the videos to the person using it. All in all, the proposed WCMIVR model acquires largest average precision percentage of 97.88, largest average recall percentage of 99.04, largest average percentage of accuracy of 98.46, largest average F-Measure percentage of 98.45 and with the lowest overall FDR measure of 0.03 which makes WCMIVR model the best classifier for web video recommendation which is compliant to Web 3.0.

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Authors and Affiliations

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

    Beulah Divya Kannan

  2. Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

    Gerard Deepak

Authors
  1. Beulah Divya Kannan
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  2. 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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Kannan, B.D., Deepak, G. (2023). WCMIVR: A Web 3.0 Compliant Machine Intelligence Driven Scheme for Video 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_53

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