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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gulzar, Z., Anny Leema, A., Deepak, G.: PCRS: personalized course recommender system based on hybrid approach. In: 6th International Conference on Smart Computing and Communications, ICSCC 2017, 7–8 December 2017, Kurukshetra, India (2017)
Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., Sun, J.: Hierarchical reinforcement learning for course recommendation in MOOCs. In: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Ma, H., Wang, X., Hou, J., Lu, Y.: Course recommendation based on semantic similarity analysis. In: 2017 IEEE 3rd International Conference on Control Science and Systems Engineering
Ibrahim, M.E., Yang, Y., Ndzi, D., Yang, G., Almaliki, M.: Ontology-based Personalised Course Recommendation Framework IEEE Access. https://doi.org/10.1109/ACCESS.2018.2889635
Lynn, N.D., Emanuel, A.W.R.: A review on Recommender Systems for course selection in higher education. In: The 5th Annual Applied Science and Engineering Conference (AASEC 2020). https://doi.org/10.1088/1757-899X/1098/3/032039
Al-Badarenah, A., Alsakran, J.: An automated recommender system for course selection. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 7(3) (2016)
Imran, H., Hoang, Q., Chang, T.-W., Kinshuk, Graf, S.: A framework to provide personalization in learning management systems through a recommender system approach”. ACIIDS 2014, Part I. LNAI, vol. 8397, pp. 271–280. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05476-6_28
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Eighteenth National Conference on Artificial Intelligence (AAAI-2002) (2002)
Chongchong Zhao, and Aonan Cai., “The Similarity Calculation of Concept Name”, 2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT)
Mondal, B., Patra, O., Mishra, S., Patra, P.: A course recommendation system based on grades. In: Conference: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). https://doi.org/10.1109/ICCSEA49143.2020.9132845
Ray, S., Sharma, A.: A Collaborative Filtering Based Approach for Recommending Elective Courses
Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019)
Deepak, G., Rooban, S., Santhanavijayan, A.: A knowledge centric hybridized approach for crime classification incorporating deep bi-LSTM neural network. Multimed. Tools Appl. 80(18), 28061–28085 (2021). https://doi.org/10.1007/s11042-021-11050-4
Deepak, G., Santhanavijayan, A.: UQSCM-RFD: a query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction. Neural Computing and Applications, 1–25 (2021)
Krishnan, N., Deepak, G.: Towards a novel framework for trust driven web URL recommendation incorporating semantic alignment and recurrent neural network. In: 2021 7th International Conference on Web Research (ICWR), pp. 232–237. IEEE, May 2021
Roopak, N., Deepak, G.: OntoKnowNHS: ontology driven knowledge centric novel hybridised semantic scheme for image recommendation using knowledge graph. In: Iberoamerican Knowledge Graphs and Semantic Web Conference, pp. 138–152. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91305-2_11
Ojha, R., Deepak, G.: Metadata driven semantically aware medical query expansion. In: Iberoamerican Knowledge Graphs and Semantic Web Conference, pp. 223–233. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91305-2_17
Surya, D., Deepak, G.: USWSBS: user-centric sensor and web service search for IoT application using bagging and sunflower optimization. In: International Conference on Emerging Trends and Technologies on Intelligent Systems, pp. 349–359. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-3097-2_29
Arulmozhivarman, M., Deepak, G.: OWLW: ontology focused user centric architecture for web service recommendation based on LSTM and whale optimization. In: European, Asian, Middle Eastern, North African Conference on Management & Information Systems, pp. 334–344. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8_32
Adithya, V., Deepak, G., Santhanavijayan, A.: HCODF: hybrid cognitive ontology driven framework for socially relevant news validation. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 731–739. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_66
Rithish, H., Deepak, G., Santhanavijayan, A.: Automated assessment of question quality on online community forums. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 791–800. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_72
Aditya, S., Muhil Aditya, P., Deepak, G., Santhanavijayan, A.: IIMDR: intelligence integration model for document retrieval. In: International Conference on Digital Technologies and Applications pp. 707–717. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_64
Tiwari, S., Al-Aswadi, F.N., Gaurav, D.: Recent trends in knowledge graphs: theory and practice. Soft Comput. 25(13), 8337–8355 (2021)
Abhishek, K., Pratihar, V., Shandilya, S.K., Tiwari, S., Ranjan, V.K., Tripathi, S.: An intelligent approach for mining knowledge graphs of online news. Int. J. Comput. Appl., 1–9 (2021)
Tiwari, S., Gaurav, D., Srivastava, A., Rai, C., Abhishek, K.: A preliminary study of knowledge graphs and their construction. In: Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, vol. 3, pp. 11–20. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9774-9_2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-01942-5_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-01941-8
Online ISBN: 978-3-031-01942-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)