Advertisement

Social Navigation Support in a Course Recommendation System

  • Rosta Farzan
  • Peter Brusilovsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)

Abstract

The volume of course-related information available to students is rapidly increasing. This abundance of information has created the need to help students find, organize, and use resources that match their individual goals, interests, and current knowledge. Our system, CourseAgent, presented in this paper, is an adaptive community-based hypermedia system, which provides social navigation course recommendations based on students’ assessment of course relevance to their career goals. CourseAgent obtains students’ explicit feedback as part of their natural interactivity with the system. This work presents our approach to eliciting explicit student feedback and then evaluates this approach.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bretzke, H., Vassileva, J.: Motivating cooperation on peer to peer networks. In: Proceeding of 9th International Conference on User Modeling (2003)Google Scholar
  2. 2.
    Brusilovsky, P.: Adaptive hypermedia. User Modeling and User Adapted Interaction 11(1/2), 87–110 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Cheng, R., Vassileva, J.: Adaptive Reward Mechanism for Sustainable Online Learning Community. In: Proceedings of 12th International Conference on Artificial Intelligence in Education, AIED 2005 (2005)Google Scholar
  4. 4.
    Claypool, M., Le, P., Waseda, M., Brown, D.: Implicit interest indicators. In: Proceedings of ACM Intelligent User Interfaces (IUI 2001) (2001)Google Scholar
  5. 5.
    Dieberger, A., Dourish, P., Höök, K., Resnick, P., Wexelblat, A.: Social navigation: Techniques for building more usable systems. Interactions 7(6), 36–45 (2000)CrossRefGoogle Scholar
  6. 6.
    Smyth, B., Balfe, E., Freyne, J., Briggs, P., Coyle, M., Boydell, O.: Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine. User Modeling & User-Adapted Interaction 14(5), 383–423 (2004)CrossRefGoogle Scholar
  7. 7.
    Harper, F.M., Li, X., Chen, Y., Konstan, J.: An economic model of user rating in an online recommender system. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) Proceedins of 10th International Conference on User Modeling (UM 2005), Edinburgh, Scotland, UK (2005)Google Scholar
  8. 8.
    Ling, K., Beenen, G., Ludford, P., Wang, X., Chang, K., Li, X., Cosley, D., Frankowski, D., Terveen, L., Rashid, A.M., Resnick, P., Kraut, R.: Using social psychology to motivate contributions to online communities. Journal of Computer-Mediated Communication 10(4) article 10 (2005)Google Scholar
  9. 9.
    Miller, B., Albert, I., Lam, S.K., Konstan, J., Riedl, J.: MovieLens Unplugged: Experiences with a Recommender System on Four Mobile Devices. In: Proceedings of the 17th Annual Human-Computer Interaction Conference (2003)Google Scholar
  10. 10.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of 10th International World Wide Web conference (2001)Google Scholar
  11. 11.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating ‘word of mouth’. In: Proceedings of Computer Human Interaction (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rosta Farzan
    • 1
  • Peter Brusilovsky
    • 1
    • 2
  1. 1.Intelligent Systems Program and University of PittsburghPittsburghUSA
  2. 2.School of Information SciencesUniversity of PittsburghPittsburghUSA

Personalised recommendations