Abstract
Social learning is considered one of the most prevalent disciplines within e-learning. To handle learning resources within social environments, recommendation systems are gaining tremendous prominence based on a series of criteria such as the rate of learner interaction with the learning environment. On the basis of this, we highlight an overriding issue focusing on integrating the variety of activities carried out by learners within the learning environment. The challenge with most recommendation systems is that they do not address multiple activities performed by learners which may significantly affect recommendations. In our study, we focus on this point while proposing a hybrid system combining the two parameters of correlation and co-occurrence. The objective is to pinpoint the degree of connectivity between activities and their influence on recommendations. We carried out our study on an available database carried out by a Chinese team. We thus compare the results of the hybrid system with the non-hybrid system based solely on co-occurrence by measuring the performance of the two approaches. It is demonstrated that the proposed hybrid system produces highly gratifying results compared to the non-hybrid system.
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
Kulkarni, P.V., Rai, S., Kale, R.: Recommender system in eLearning: a survey. In: Bhalla, S., Kwan, P., Bedekar, M., Phalnikar, R., Sirsikar, S. (eds.) Proceeding of International Conference on Computational Science and Applications, pp. 119–126. Springer, Singapore (2020)
Abbas, M., Riaz, M.U., Rauf, A., Khan, M.T., Khalid, S.: Context-aware Youtube recommender system. In: 2017 International Conference on Information and Communication Technologies (ICICT), Karachi, December 2017, pp. 161–164 (2017). https://doi.org/10.1109/ICICT.2017.8320183
Lund, J., Ng, Y.-K.: Movie recommendations using the deep learning approach. In: 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, July 2018, pp. 47–54 (2018). https://doi.org/10.1109/IRI.2018.00015
Wang, X., Wang, C.: Recommendation system of ecommerce based on improved collaborative filtering algorithm. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (2017)
Pujahari, A., Padmanabhan, V.: An approach to content based recommender systems using decision list based classification with k-DNF rule set. In: 2014 International Conference on Information Technology, Bhubaneswar, India, December 2014, pp. 260–263 (2014). https://doi.org/10.1109/ICIT.2014.13
Zhang, R., Liu, Q., Chun-Gui, Wei, J.-X., Huiyi-Ma: Collaborative filtering for recommender systems. In: 2014 Second International Conference on Advanced Cloud and Big Data, Huangshan, China, November 2014, pp. 301 308 (2014). https://doi.org/10.1109/CBD.2014.47
Shokeen, J., Rana, C.: A study on features of social recommender systems. Artif Intell Rev 53, 965–988 (2020). https://doi.org/10.1007/s10462-019-09684-w
Guy, I.: Social recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 511–543. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_15
Ahmadian, S., Joorabloo, N., Jalili, M., Ren, Y., Meghdadi, M., Afsharchi, M.: A social recommender system based on reliable implicit relationships. Knowl.-Based Syst. 192, 105371 (2020). https://doi.org/10.1016/j.knosys.2019.105371
Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R.: Social recommender systems. In: Learning Automata Approach for Social Networks. Studies in Computational Intelligence, pp. 281–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10767-3_8
Revathy, V.R., Pillai, A.S.: Classification and applications of social recommender systems. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds.) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol. 614. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60618-7_70
Turnip, R., Nurjanah, D., Kusumo, D.S.: Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners’ rating. In: 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), Miri, Sarawak, Malaysia, November 2017, pp. 61–66 (2017). https://doi.org/10.1109/IC3e.2017.8409239
Perumal, S.P., Arputharaj, K., Sannasi, G.: Fuzzy family tree similarity based effective e-learning recommender system. In: 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India, January 2017, pp. 146–150 (2017). https://doi.org/10.1109/ICoAC.2017.7951760
Mohamed, M.H., Khafagy, M.H., Ibrahim, M.H.: Recommender systems challenges and solutions survey. In: 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt, February 2019, pp. 149–155 (2019). https://doi.org/10.1109/ITCE.2019.8646645
Wintrode, J., Sell, G., Jansen, A., Fox, M., Garcia-Romero, D., McCree, A.: Content-based recommender systems for spoken documents. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Queensland, Australia, April 2015, pp. 5201–5205 (2015). https://doi.org/10.1109/ICASSP.2015.7178963
Mustafa, N., Ibrahim, A.O., Ahmed, A., Abdullah, A.: Collaborative filtering: techniques and applications. In: 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), Khartoum, Sudan, January 2017, pp. 1–6 (2017). https://doi.org/10.1109/ICCCCEE.2017.7867668
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Souabi, S., Retbi, A., Khalidi Idrissi, M., Bennani, S. (2021). A Novel Hybrid Recommendation Approach Based on Correlation and Co-occurrence Between Activities Within Social Learning Network. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-75100-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75099-2
Online ISBN: 978-3-030-75100-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)