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HSIL: Hybrid Semantic Infused Learning Approach for Course Recommendation

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Digital Technologies and Applications (ICDTA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 454))

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Abstract

Course recommendation is essential to enable learners to choose the best course among many available courses. So, as a result, a semantically driven course recommendation system is required as the data over the world wide web is increasing, and the web is also tending towards Semantic Web 3.0. In this approach, the initial term pool is created based on the existing user learner profile and the current clicks of the user to determine the exact needs of the user for the term enrichment is done by incorporating ontologies of courses and metadata generation approach is proposed where the metadata is initially classified using XGBoost algorithm where the top 25% is used. Then, the core dataset is also classified using the XGBoost algorithm based on the enriched user terms. And then, the semantic similarity is computed using concept similarity under the genetic algorithm and is recommended to the user. Overall, the accuracy of 96.3% is achieved.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India

    Dev Agrawal

  2. National Institute of Technology, Tiruchirappalli, India

    Gerard Deepak

Authors
  1. Dev Agrawal
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  2. Gerard Deepak
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Correspondence to Gerard Deepak .

Editor information

Editors and Affiliations

  1. Ecole Nationale des Sciences Appliquées, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

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Agrawal, D., Deepak, G. (2022). HSIL: Hybrid Semantic Infused Learning Approach for Course Recommendation. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-01942-5_42

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