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The Adaptive Web pp 377–408Cite as

Hybrid Web Recommender Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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Authors and Affiliations

  1. School of Computer Science, Telecommunications and Information Systems, DePaul University, 243 S. Wabash Ave., Chicago, Illinois, USA

    Robin Burke

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  1. Robin Burke
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Peter Brusilovsky Alfred Kobsa Wolfgang Nejdl

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Burke, R. (2007). Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds) The Adaptive Web. Lecture Notes in Computer Science, vol 4321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_12

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  • DOI: https://doi.org/10.1007/978-3-540-72079-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72078-2

  • Online ISBN: 978-3-540-72079-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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