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Hybrid Web Recommender Systems

  • Robin Burke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4321)

Abstract

Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.

Keywords

Recommender System User Profile Knowledge Source Collaborative Filter Feature Combination 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Robin Burke
    • 1
  1. 1.School of Computer Science, Telecommunications and Information Systems, DePaul University, 243 S. Wabash Ave., Chicago, IllinoisUSA

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