Skip to main content
SpringerLink
Log in

Towards a Recommender System Based on Community Detection and Performed Activities in the Context of Social Learning

  • Chapter
  • First Online:
Learning with Technologies and Technologies in Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 456))

  • 962 Accesses

Abstract

Social online learning is gaining prominence with the emergence of social media and networks. In the presence of recommendation systems, the learner has access to several educational contents and elements proposed according to his interaction, profile, dynamics and other parameters. Our proposal revolves around an intelligent recommender system for generating recommendations in social learning environments, in particular social networks, based on the learner's activities, falling into the category of hybrid recommender systems and integrating the concept of community detection in the generation of recommendations. The proposed global approach is composed of several recommender systems: the correlation and co-occurrence based recommender system aims at measuring the degree of connectivity between learners' activities in order to generate relevant recommendations in the light of learners' implicit feedback, the community detection based recommender system through friendships. This system is an important part of our overall approach as it combines the hybrid recommendation system with the community detection based on friendship links between learners and the recommendation system based on the degree of interactivity of learners with the available learning objects. This system also consists in detecting communities, which group learners with similar preferences and orientations. The results obtained prove the potential of combining the two notions of connectivity and the integration of community detection based on several indicators to produce more relevant recommendations, while offering recommendations by group of learners by applying the concept of continuous improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. García-Peñalvo FJ (2021) Avoiding the dark side of digital transformation in teaching. An institutional reference framework for elearning in higher education. Sustainability 13(4) https://doi.org/10.3390/su13042023

  2. de Souza Rodrigues MA, Chimenti P, Nogueira ARR An exploration of eLearning adoption in the educational ecosystem. Educ Inform Technol 26(1):585–615. https://doi.org/10.1007/s10639-020-10276-3.

  3. Safdari M Contributions of Edmodo social learning network to Iranian EFL Learners. Writing Accu 19

    Google Scholar 

  4. Brady WJ, McLoughlin KL, Doan TN, Crockett M (2021) How social learning amplifies moral outrage expression in online social networks. PsyArXiv https://doi.org/10.31234/osf.io/gf7t5

  5. Zhang Q, Lu J, Jin Y (2021) Artificial intelligence in recommender systems. Comp Intell Syst 7(1):439–457. https://doi.org/10.1007/s40747-020-00212-w

    Article  Google Scholar 

  6. Felfernig A, Tran TNT, Le V-M (2021) Recommender systems beyond E-commerce: presence and future. In: Dutta T, Mandal MK (eds) Consumer happiness: multiple perspectives. Springer Singapore, Singapore, pp 203–230. https://doi.org/10.1007/978-981-33-6374-8_14

  7. Zhang Q, Lu J, Zhang G Recommender systems in E-learning. p 14

    Google Scholar 

  8. Pérez-Almaguer Y, Yera R, Alzahrani AA, Martínez L (2021) Content-based group recommender systems: a general taxonomy and further improvements. Expert Syst Appl 184:115444. https://doi.org/10.1016/j.eswa.2021.115444

    Article  Google Scholar 

  9. Wenga C, Fansi M, Chabrier S, Mari J-M, Gabillon A A comprehensive review on non-neural networks collaborative filtering recommendation systems. p 29

    Google Scholar 

  10. Abu-Salih B, Alsawalqah H, Elshqeirat B, Issa T, Wongthongtham P, Premi KK (2021) Toward a knowledge-based personalised recommender system for mobile app development. jucs 27(2):208–229. https://doi.org/10.3897/jucs.65096

  11. Guruge DB, Kadel R, Halder SJ (2021) The state of the art in methodologies of course recommender systems—a review of recent research. Data 6(2) https://doi.org/10.3390/data6020018

  12. Amane M, Aissaoui K, Berrada M (2021) A multi-agent and content-based course recommender system for university e-learning platforms. Digit Technol Appl Cham 663–672

    Google Scholar 

  13. Saraswat M, Chakraverty S (2021) Emotion distribution profile for movies recommender systems. Communication and Intelligent Systems, Singapore, pp 365–373

    Google Scholar 

  14. Fortes RS, de Sousa DX, Coelho DG, Lacerda AM, Gonçalves MA (2021) Individualized extreme dominance (IndED): a new preference-based method for multi-objective recommender systems. Inf Sci 572:558–573. https://doi.org/10.1016/j.ins.2021.05.037

    Article  MathSciNet  Google Scholar 

  15. El-sayed AA, Ibrahim LF, Salman HA Recommender system approaches: a survey. p 8

    Google Scholar 

  16. Van DN, Pham VT, Thanh TM (2020) Robust content-based recommendation distribution system with gaussian mixture model. Advances in Computational Collective Intelligence, Cham, pp 199–211

    Google Scholar 

  17. Jain A, Nagar S, Singh PK, Dhar J (2020) EMUCF: enhanced multistage user-based collaborative filtering through non-linear similarity for recommendation systems. Expert Syst Appl 161:113724. https://doi.org/10.1016/j.eswa.2020.113724

    Article  Google Scholar 

  18. Ayala C, Jimenez K, Loza-Aguirre E, Andrade RO (2021) A hybrid recommender system for cybersecurity based on a rating approach. In: Advances in security, networks, and internet of things. Cham, pp 397–409

    Google Scholar 

  19. Kowald D, Lacic E, Theiler D, Lex E (2019) AFEL-REC: a recommender system for providing learning resource recommendations in social learning environments. arXiv:1808.04603 [cs] Retrieved 18 May, 2019, http://arxiv.org/abs/1808.04603

  20. Brik M, Touahria M (2020) Contextual information retrieval within recommender system: case study “E-learning System”’. TEM J 1150–1162 https://doi.org/10.18421/TEM93-41

  21. Chen X, Yue X-G, Li RYM, Zhumadillayeva A, Liu R (2021) Design and application of an improved genetic algorithm to a class scheduling system. Int J Emerg Technol Learn 16(01):44. https://doi.org/10.3991/ijet.v16i01.18225

    Article  Google Scholar 

  22. Bhaskaran S, Marappan R, Santhi B (2021) Design and analysis of a cluster-based intelligent hybrid recommendation system for E-learning applications. Mathematics 9(2) https://doi.org/10.3390/math9020197

  23. Masethe MA, Ojo SO, Odunaike SA, Masethe HD (2021) Framework of recommendation systems for educational data mining (EDM) methods: CBR-RS with KNN Implementation. In: Transactions on Engineering Technologies, Singapore, pp 87–98

    Google Scholar 

  24. Lu B (2012) Community detection algorithm using the definition of community. In: 2012 International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP). Chengdu, China, pp 16–19. https://doi.org/10.1109/ICWAMTIP.2012.6413429

  25. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 2008(10):P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

    Article  MATH  Google Scholar 

  26. Rosvall M, Axelsson D, Bergstrom CT (2009) The map equation. Eur. Phys. J. Spec. Top. 178(1):13–23. https://doi.org/10.1140/epjst/e2010-01179-1

    Article  Google Scholar 

  27. Pons P, Latapy M Computing communities in large networks using random walks. p 20

    Google Scholar 

  28. Cuzzocrea A, Papadimitriou A, Katsaros D, Manolopoulos Y (2012) Edge betweenness centrality: a novel algorithm for QoS-based topology control over wireless sensor networks. J Netw Comput Appl 35(4):1210–1217. https://doi.org/10.1016/j.jnca.2011.06.001

    Article  Google Scholar 

  29. Martín E, Gértrudix M, Urquiza-Fuentes J, Haya PA (2015) Student activity and profile datasets from an online video-based collaborative learning experience. Br J Educ Technol 46(5):993–998. https://doi.org/10.1111/bjet.12318

    Article  Google Scholar 

  30. Feng C, Liang J, Song P, Wang Z (2020) A fusion collaborative filtering method for sparse data in recommender systems. Inf Sci 521:365–379. https://doi.org/10.1016/j.ins.2020.02.052

    Article  MathSciNet  Google Scholar 

  31. https://www.kaggle.com/gspmoreira/articles-sharing-reading-from-cit-deskdrop

Download references

Author information

Authors and Affiliations

  1. Rime Team-Networking, Modeling and E-Learning Team-MASI Laboratory-Engineering.3S Research Center, Mohammadia School of Engineers (EMI), Mohammed V University In Rabat Morocco, Rabat, Morocco

    Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi & Samir Bennani

Authors
  1. Sonia Souabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asmaâ Retbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Khalidi Idrissi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samir Bennani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sonia Souabi .

Editor information

Editors and Affiliations

  1. CTI Global, Frankfurt, Germany

    Michael E. Auer

  2. Faculty of Informatics and Computer Science, The British University in Egypt, Cairo, Egypt

    Andreas Pester

  3. College of Engineering, University of Georgia, Athens, GA, USA

    Dominik May

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Souabi, S., Retbi, A., Khalidi Idrissi, M., Bennani, S. (2022). Towards a Recommender System Based on Community Detection and Performed Activities in the Context of Social Learning. In: Auer, M.E., Pester, A., May, D. (eds) Learning with Technologies and Technologies in Learning. Lecture Notes in Networks and Systems, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-031-04286-7_24

Download citation

Publish with us

Policies and ethics

Search

Navigation