Abstract
Video recommendation systems in e-learning platforms are a specific type of recommendation system that use algorithms to suggest educational videos to students based on their interests and preferences. Student’s written feedback or reviews can provide more detailed about the educational video, including its strengths and weaknesses. In this paper, we build education video recommender system based on learners’ reviews. We use LDA topic model on textual data extracted from educational videos to train language modle as an input to supervised CNN model. Additionally, we used latent factor modle on extracts the educational videos’ features and learner preference from learners’ historical data as an output CNN model. In Our proposed technique, we hybrid user ratings and reviews to tackle sparsity and cold start problem in recommender system. Our recommender use user review to suggest a new recommended videos, but in case there is no review (empty cell in matrix factorization) or unclear comment then we will take user rating on that educational video. We work on real-world big and heterogynous dataset from coursera. Result shows that new production rating from learners reviews can be used to make good new recommended videos to student that not previously seen and reduce cold start and sparsity problem affects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hazar, M.J., Toman, Z.H., Toman, S.H.: Automated scoring for essay questions in E-learning. J. Phys.: Conf. Ser. 1294(4) (2019). https://doi.org/10.1088/1742-6596/1294/4/042014
Li, L.Y.: Effect of prior knowledge on attitudes, behavior, and learning performance in video lecture viewing. Int. J. Hum. Comput. Interact. 35(4–5), 415–426 (2018). https://doi.org/10.1080/10447318.2018.1543086
Ali, A.A.M.A., Mabrouk, M., Zrigui, M.: A review: blockchain technology applications in the field of higher education. J. Hunan Univ. Nat. Sci. 49(10), 88–99 (2022). https://doi.org/10.55463/ISSN.1674-2974.49.10.10
Legrand, A., Trystram, D., Zrigui, S.: Adapting batch scheduling to workload characteristics: what can we expect from online learning? In 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 686–695. IEEE (2019)
Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-adapt. Interact. 25(2), 99–154 (2015). https://doi.org/10.1007/S11257-015-9155-5/TABLES/4
Jaballi, S., Zrigui, S., Sghaier, M.A., Berchech, D., Zrigui, M.: Sentiment analysis of Tunisian users on social networks: overcoming the challenge of multilingual comments in the Tunisian dialect. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds.) ICCCI 2022. LNCS (LNAI and LNB), vol. 13501, pp. 176–192. Springer, CHam (2022). https://doi.org/10.1007/978-3-031-16014-1_15
Mahmoud, A., Zrigui, M.: Semantic similarity analysis for corpus development and paraphrase detection in Arabic. Int. Arab J. Inf. Technol. 18(1), 1–7 (2021). https://doi.org/10.34028/iajit/18/1/1
Gomathi, R.M., Ajitha, P., Krishna, G.H.S., Pranay, I.H.: Restaurant recommendation system for user preference and services based on rating and amenities. In: ICCIDS 2019–2nd International Conference on Computational Intelligence in Data Science Processing (2019). https://doi.org/10.1109/ICCIDS.2019.8862048
Musto, C., de Gemmis, M., Lops, P., Semeraro, G.: Generating post hoc review-based natural language justifications for recommender systems. User Model. User-Adapt. Interact. 31(3), 629–673 (2021). https://doi.org/10.1007/S11257-020-09270-8/TABLES/19
Sghaier, M.A., Zrigui, M.: Sentiment analysis for Arabic E-commerce websites. In: 2016 International Conference on Engineering MIS (ICEMIS), pp. 1–7 (2016). https://doi.org/10.1109/ICEMIS.2016.7745323
Fraihat, S., Shambour, Q.: A framework of semantic recommender system for e-learning (2015). ammanu.edu.jo, https://www.ammanu.edu.jo/english/pdf/StaffResearch/IT/923/00. Accessed 12 Feb 2023. Paper-A Framework of Semantic Recommender System for elearning.pdf
Choudrey, S.: Video recommendation through machine learning in Amazon web services (2021). http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303010. Accessed 12 Feb 2023
Slimi, A., Hamroun, M., Zrigui, M., Nicolas, H.: Emotion recognition from speech using spectrograms and shallow neural networks. In: Proceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia, pp. 35–39 (2020)
Ayadi, R., Maraoui, M., Zrigui, M.: Latent topic model for indexing Arabic documents. Int. J. Inf. Retrieval Res. (IJIRR) 4(2), 57–72 (2014). https://doi.org/10.4018/IJIRR.2014040104, https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2014040104
Hazar, M.J., Zrigui, M., Maraoui, M.: Learner comments-based recommendation system. Procedia Comput. Sci. 207, 2000–2012 (2022). https://doi.org/10.1016/J.PROCS.2022.09.259
Treude, C., Sicard, M., Klocke, M., Robillard, M.: TaskNav: task-based navigation of software documentation. Proc. - Int. Conf. Softw. Eng. 2, 649–652 (2015). https://doi.org/10.1109/ICSE.2015.214
Bsir, B., Zrigui, M.: Bidirectional LSTM for author gender identification. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) ICCCI 2018. LNCS (LNAI), vol. 11055, pp. 393–402. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98443-8_36
Zhang, Y., Liu, R., Li, A.D.: A novel approach to recommender system based on aspect-level sentiment analysis (2016). atlantis-press.com, https://www.atlantis-press.com/proceedings/nceece-15/25847127. Accessed 21 June 2022
Hutto, C.J., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text (2014). http://sentic.net/. Accessed 21 June 2022
Maraoui, M., Antoniadis, G., Zrigui, M.: CALL System for Arabic based on natural language processing tools. In: IICAI, pp. 2249–2258 (2009)
Ayadi, R., Maraoui, M., Zrigui, M.: Intertextual distance for Arabic texts classification. In: 2009 International Conference for Internet Technology and Secured Transactions (ICITST), pp. 1–6. IEEE (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hazar, M.J., Abid Muslam Abid Ali, A., Zrigui, S., Maraoui, M., Mabrouk, M., Zrigui, M. (2023). Educational Videos Recommendation System Based on Topic Modeling. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-41456-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41455-8
Online ISBN: 978-3-031-41456-5
eBook Packages: Computer ScienceComputer Science (R0)