Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems

  • Alessandro da S. DiasEmail author
  • Leandro K. Wives
Original Article


In this paper, we present a recommendation approach for learning objects (LOs) in ubiquitous e-learning systems. Many of these systems are social learning networks, and learners can interact with other users through forums or chats. In these systems, learners usually perform a set of choices or make decisions (“what to learn”, “how to learn”, “with whom to learn”, among others) during learning, depending on the system. The developed approach uses the result of these choices as a source of information. It is an extension of the User-based Nearest Neighbor recommendation approach, which has roots in the Nearest Neighbor search problem. Moreover, this approach uses social signals, interests, and preferences of learner users. With the fusion of these elements, we sought to find the most similar users to the active user, and then, to generate more accurate recommendations. We present an experimental evaluation of this approach showing that the usage prediction accuracy varies according to the combination of user choices and presents statistically significant higher prediction than baseline approaches. Despite being focused on ubiquitous e-learning systems, we briefly discuss how to use it in other domains where we observe that users can make decisions when interacting with other systems.


Social Recommender System Nearest Neighbor search Multidimensional user preference model Ubiquitous e-learning systems Social learning networks Learner-driven learning 



This work is partially supported by CNPq (Brazilian Council for Scientific and Technological Development), and CAPES.


  1. 1.
    Adomavicius G, Tuzhilin A (2015) Context-aware recommender systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, BostonGoogle Scholar
  2. 2.
    Alexander A, Kernohan W, Mccullagh P (2004) Self directed and lifelong learning. Global Health Informatics Education - Stud Health Technol Inform 109:152–166Google Scholar
  3. 3.
    Amatriain X, Pujol JM (2015) Data mining methods for recommender systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, BostonGoogle Scholar
  4. 4.
    Bell RM, Koren Y (2007) Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Proceedings of the 2007 Seventh IEEE International Conference on Data Mining (ICDM '07). IEEE Computer Society, Washington, DC, pp 43–52. Google Scholar
  5. 5.
    Bhargava P, Phan T, Zhou J, Lee J (2015) Who, what, when, and where: multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data. In Proceedings of the 24th International Conference on World Wide Web (WWW'15). International World Wide Web Conferences Steering Committee, Switzerland, p 130–140Google Scholar
  6. 6.
    Bill & Melinda Gates Foundation, the Michael & Susan Dell Foundation, Silicon Schools, EDUCAUSE, iNACOL, and others. Personalized learning: a working definition. 2014. Education Week. V.34, Issue 09, Page s4. Retrieved from Accessed 3 May 2018
  7. 7.
    Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (UAI’98)Google Scholar
  8. 8.
    Chen L, Pu P (2010) Eye-tracking study of user behavior in recommender interfaces. Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. UMAP 2010: User Modeling, Adaptation, and Personalization,, p 375–380Google Scholar
  9. 9.
    Dey A, Abowd G, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications, HumAN-Computer Interaction, vol 16, pp 97–166, Dec.Google Scholar
  10. 10.
    Dias SA, Wives KL (2018) Assessment of the most relevant learning object metadata - relieving the learner-user from information overload. Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), vol 1. p 175–182Google Scholar
  11. 11.
    Dias SA, Wives KL (2018) Definition of learner choices from learner-driven learning for ubiquitous e-learning systems and its application in the AdaptWeb platform. Proceedings of the 29th Brazilian Symposium on Informatics in Education (SBIE'2018), Fortaleza, Brazil. AcceptedGoogle Scholar
  12. 12.
    Dourish P (2004) What we talk about when we talk about context. Pers Ubiquit Comput 8:19–30CrossRefGoogle Scholar
  13. 13.
    Drachsler H, Verbert K, Santos O, Manouselis N (2015) Panorama of recommender systems to support learning. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, BostonGoogle Scholar
  14. 14.
    Draft Standard for Learning Object Metadata (2002) Retrieved from Accessed 3 May 2018
  15. 15.
    Durao F, Dolog P (2009) Social and behavioral aspects of a tag-based recommender system. Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, ISDA’09Google Scholar
  16. 16.
    Fazeli S, Loni B, Drachsler H, Sloep P (2014) Which recommender system can best fit social learning platforms? Proceedings of the 9th European Conference on Open Learning and Teaching in Educational Communities - Volume 8719 (EC-TEL 2014), Christoph Rensing, Sara Freitas, Tobias Ley, and Pedro Muñoz-Merino (Eds.), vol 8719. Springer-Verlag New York, Inc., New York, pp 84–97.
  17. 17.
    Fazeli S, Rajabi E, Lezcano L, Drachsler H, Sloep P (2016)Supporting users of open online courses with recommendations: an algorithmic study. Proceedings of the IEEE 16th International Conference on Advanced Learning Technologies (ICALT), Austin, TX, 2016, pp 423–427.
  18. 18.
    Garcia V, Debreuve E, Barlaud M (2008) Fast k nearest neighbor search using GPU. Computer Vision and Pattern Recognition Workshops. CVPRW'08. IEEE Computer Society Conference on. IEEE, 2008Google Scholar
  19. 19.
    Ginsberg MB (2015) Excited to learn: motivation and culturally responsive teaching. Corwin Press, Thousand OaksGoogle Scholar
  20. 20.
    Jameson A, Willemsen MC, Felfernig A, Gemmis M, Lops P, Semeraro G, Chen L (2015) Human decision making and recommender systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, BostonGoogle Scholar
  21. 21.
    Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, New YorkCrossRefGoogle Scholar
  22. 22.
    Jannach D, Resnick P, Tuzhilin A, Zanker M (2016) Recommender systems — beyond matrix completion. Commun ACM 59(11 (October 2016)):94–102. CrossRefGoogle Scholar
  23. 23.
    Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8 (August 2009)):30–37.
  24. 24.
    Kusner M, Tyree S, Weinberger K, Agrawal K (2014) Stochastic neighbor compression. In International Conference on Machine Learning pp 622–630Google Scholar
  25. 25.
    Li L, Zheng Y, Ogata H, Yano Y (2014) A framework of ubiquitous learning environment, Proceedings of the Fourth Int Conf Computer and Information Technology (CIT ‘04), p 345–350Google Scholar
  26. 26.
    Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst 56(January 2014):156–166CrossRefGoogle Scholar
  27. 27.
    Manouselis N, Vuorikari R, Assche FV (2010) Collaborative recommendation of e-learning resources: an experimental investigation. J Comput Assist Learn 26(4):227–242CrossRefGoogle Scholar
  28. 28.
    Manouselis N, Drachsler H, Verbert K, Duval E (2012) Recommender systems for learning. Springer Publishing Company, Incorporated, New YorkGoogle Scholar
  29. 29.
    Miliband D (2006) Choice and voice in personalised learning. IN OECD, Personalizing educationGoogle Scholar
  30. 30.
    Palazzo JMO, Brunetto M, Proença JM, Pimenta MS, Pinto CHF, Lima JV, Freitas V, Marçal VP, Gasparine I, Amaral M (2003) AdaptWeb: um Ambiente para Ensino-aprendizagem Adaptativo na Web. Educar em revista, Curitiba, número especial, p 175–197Google Scholar
  31. 31.
    Powell A, Kennedy K, Patrick S (2013) Mean what you say: defining and integrating personalized, blended and competency education Retrieved from Accessed 3 May 2018
  32. 32.
    Rotărescu E (2011) Applying PERT and critical path method in human resource training. Review of General Management, Braşov, acceptat pentru publicare în, 7Google Scholar
  33. 33.
    Saha S, Ghrera SP (2015) Nearest Neighbor search in Complex Network for Community Detection. arXiv preprint arXiv:1511.07210. Retrieved from Accessed 3 May 2018
  34. 34.
    Shani G, Gunawardana A (2015) Evaluating recommendation systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, BostonGoogle Scholar
  35. 35.
    Symeonidis P, Ntempos D, Manolopoulos Y (2014) Recommender systems for location-based social networks. Springer Briefs in Electrical and Computer Engineering, ChamCrossRefGoogle Scholar
  36. 36.
    Takács G, Pilászy I, Németh B, Tikk D (2009) Scalable collaborative filtering approaches for large recommender systems. J Mach Learn Res 10(June 2009):623–656Google Scholar
  37. 37.
    Takano K, Li KF (2009) An adaptive personalized recommender based on web-browsing behavior learning. In Proceedings of the 23rd International Conference on Advanced Information Networking and Applications, AINA 2009, Workshops Proceedings, Bradford, United Kingdom.
  38. 38.
    The LEADLAB Project (2010) Retrieved from Accessed 3 May 2018
  39. 39.
    Verbert K, Drachsler H, Manouselis N, Wolpers M, Vuorikari R, Duval E (2011) Dataset-driven research for improving recommender systems for learning. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK’11). ACM, New York, pp 44–53Google Scholar
  40. 40.
    Watkins C, Carnell E, Lodge C (2007) Effective learning in classrooms. Paul Chapman Publishing, LondonCrossRefGoogle Scholar
  41. 41.
    Zhuhadar L, Butterfield J (2014) Analyzing students logs in open online courses using SNA techniques. Proceedings of the 20th Americas Conference on Information SystemsGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Informatics InstituteUniversidade Federal do Rio Grande do SulPorto AlegreBrazil

Personalised recommendations