A Fuzzy Approach to Multidimensional Context Aware e-Learning Recommender System

  • Pragya Dwivedi
  • Kamal K. Bharadwaj
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

Traditional e-Learning recommender systems (EL-RS) based on two dimensions- learners and learning resources, help learners in alleviating information overload by providing suitable learning resources from a potentially overwhelming variety of choices. E-learning recommender systems have received considerable attention in recent years. However, the incorporation of contextual information such as time duration and mood of a learner into the e-Learning recommendation process is still in its infancy. Such contextualization is investigated as an exemplar for EL-RS that can anticipate the learners’ requirements. Usually, the representation of learner context is subjective, imprecise and vague. In this paper, we propose a fuzzy approach to multidimensional context aware EL-RS (CA-EL-RS) that includes time duration and mood of a learner as additional dimensions for item based collaborative filtering (IB-CF). The empirical results are presented to demonstrate the effectiveness of the proposed approach in identifying better top N recommendations than traditional IB-CF.

Keywords

e-Learning Multidimensional Recommender Systems Context-Aware Recommender Systems Item-based Collaborative Filtering 

References

  1. 1.
    Wan, X., Okamoto, T.: Utilizing learning process to improve recommender system for group learning support. Neural Comput. & Applic 20, 611–621 (2011)CrossRefGoogle Scholar
  2. 2.
    Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative Filtering Adapted to Recommender Systems of E-Learning. Knowledge-Based Systems 22(4), 261–265 (2009)CrossRefGoogle Scholar
  3. 3.
    Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-Aware Recommender Systems for Learning: A Survey and Future Challenges. IEEE Tran on Learning Tech 5, 1–18 (2012)CrossRefGoogle Scholar
  4. 4.
    Shen, L., Wang, M., Shen, R.: Affective e-Learning: Using “Emotional” Data to Improve Learning in Pervasive Learning Environment. Educational Technology & Society 12(2), 176–189 (2009)Google Scholar
  5. 5.
    Isen, A.M.: Positive affect and decision making. In: Lewis, M., Haviland, J. (eds.) Handbook of emotions, p. 720. The Guilford Press, Guilford (2000)Google Scholar
  6. 6.
    Kant, V., Bharadwaj, K.K.: Fuzzy computational Models of Trust and Distrust for Enhanced Recommendations. International Journal of Intelligent Systems 28(4), 332–365 (2013a)CrossRefGoogle Scholar
  7. 7.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual Information in Recommender Systems Using A Multidimensional Approach. ACM Trans. Information Syst. 23, 103–145 (2005)CrossRefGoogle Scholar
  8. 8.
    Bharadwaj, K.K., Al-Shamri, M.Y.: Fuzzy Computational Models for Trust and Reputation Systems. Electron Commerce Res. Appl. 8(1), 37–47 (2009)CrossRefGoogle Scholar
  9. 9.
    Karypis, G.: Evaluation of Item-Based Top-N Recommendation Algorithms. In: CIKM 2001: Proc. of the Tenth International Conference on Information and Knowledge Management, pp. 247–254, New York, NY, USA (2001)Google Scholar
  10. 10.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering of Net News. In: Proc. of the ACM Conference on Computer-Supported Cooperative Work (CSCW 1994), pp. 175–186. Chapel Hill (1994)Google Scholar
  11. 11.
    Dey, A., Abowd, G., Salber, D.: A Conceptual Framework and A Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Hum.-Comput. Interact. 16, 97–166 (2001)CrossRefGoogle Scholar
  12. 12.
    Jones, G.J.F., Glasnevin, D., Gareth, I.: Challenges and Opportunities of Context-Aware Information Access. In: International Workshop on Ubiquitous Data Management, pp. 53–62 (2005)Google Scholar
  13. 13.
    Palmisano, C., Tuzhilin, A., Gorgoglione, M.: Using Context to Improve Predictive Modeling of Customers in Personalization Applications. IEEE Trans. Knowl. Data Engineering 20(11), 1535–1549 (2008)CrossRefGoogle Scholar
  14. 14.
    Berry, M.J., Linoff, G.: Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley & Sons, Inc., Chichester (1997)Google Scholar
  15. 15.
    Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Rokach, L., Shapira, B., Kantor, P., Ricci, F. (eds.) Recommender Systems Handbook: A Complete Guide for Research Scientists & Practitioners, pp. 217–250. Springer (2011)Google Scholar
  16. 16.
    Schmidt, A., Beigl, M., Gellersen, H.-W.: There is More to Context Than Location. Computers & Graphics 23(6), 893–901 (1999)CrossRefGoogle Scholar
  17. 17.
    Chen, G., Kotz, D.: A Survey of Context-Aware Mobile Computing Research. Technical report, Hanover, NH, USA (2000)Google Scholar
  18. 18.
    Darling-Hammond, L., Orcutt, S., Strobel, K., Kirsch, E., Lit, I., Martin, D.: Emotion and LearningGoogle Scholar
  19. 19.
    Moridis, C., Economides, A.A.: Mood Recognition During Online Self-Assessment Test. IEEE Transactions on Learning Technologies 2(1), 50–61 (2009)CrossRefGoogle Scholar
  20. 20.
    Domingues, A., Jorge, A., Soares, C.: Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems. Information Processing and Management 49, 698–720 (2013)CrossRefGoogle Scholar
  21. 21.
    Dwivedi, P., Bharadwaj, K.K.: Effective Resource Recommendations for E-Learning: A Collaborative Filtering Framework Based on Experience and Trust. In: Proceeding of the International Conference on Computational Intelligence and Information Technology (CIIT 2011), Pune, India, pp. 165–167 (2011)Google Scholar
  22. 22.
    Dwivedi, P., Bharadwaj, K.K.: Effective Trust-Aware E-Learning Recommender System Based on Learning Styles and Knowledge Levels (to appear)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Pragya Dwivedi
    • 1
  • Kamal K. Bharadwaj
    • 2
  1. 1.Computer Science & Engineering DepartmentMotilal Nehru National Institute of TechnologyAllahabadIndia
  2. 2.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

Personalised recommendations