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

  • Shu-Feng Pan
  • Ji-Hyun Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)


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.


Recommendation System Collaborate Filter Implicit Feedback Explicit Feedback Navigation Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shu-Feng Pan
    • 1
  • Ji-Hyun Lee
    • 1
  1. 1.Graduate School of Computational Design, NYUSTYunlinTaiwan, R.O.C

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