Skip to main content
Springer Nature Link
Log in

eDAADe: An Adaptive Recommendation System for Comparison and Analysis of Architectural Precedents

  • Conference paper
Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4018))

  • 1396 Accesses

Abstract

We built a Web-based adaptive recommendation system for students to select and suggest architectural cases when they analyze “Case Study” work within the architectural design studio course, which includes deep comparisons and analyses for meaningful architectural precedents. We applied hybrid recommendation mechanism, which is combining both content-based filtering and collaborative filtering in our suggested model. It not only retains the advantages of a content-based and collaborative filtering approach, but also improves the disadvantages found in both. We expect that the approach would be helpful for students to find relevant precedents more efficient and more precise with their preferences.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Buono, P., Costabile, M.F., Guida, S., Piccinno, A.: Integrating User Data and Collaborative Filtering in a Web Recommendation System. In: Reich, S., Tzagarakis, M.M., De Bra, P.M.E. (eds.) AH-WS 2001, SC 2001, and OHS 2001. LNCS, vol. 2266, pp. 315–321. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  2. Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  3. Popescul, A., Ungar, L.H., Pennock, D.M., Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI 2001), pp. 437–444 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Computational Design, NYUST, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, R.O.C

    Shu-Feng Pan & Ji-Hyun Lee

Authors
  1. Shu-Feng Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ji-Hyun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Knowledge and Data Engineering Group, Trinity College Dublin, Dublin, Ireland

    Vincent P. Wade

  2. School of Computer and Information Science, University of South Australia, Australia

    Helen Ashman

  3. Adaptive Information Cluster, School of Computer Science & Informatics, University College Dublin, Ireland

    Barry Smyth

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pan, SF., Lee, JH. (2006). eDAADe: An Adaptive Recommendation System for Comparison and Analysis of Architectural Precedents. In: Wade, V.P., Ashman, H., Smyth, B. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2006. Lecture Notes in Computer Science, vol 4018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768012_53

Download citation

  • DOI: https://doi.org/10.1007/11768012_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34696-8

  • Online ISBN: 978-3-540-34697-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation