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Case-Based Recommendation

  • Chapter
The Adaptive Web

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4321))

Abstract

Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems.

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  1. The School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Barry Smyth

  2. ChangingWorlds Ltd., South County Business Park, Leopardstown, Dublin 18, Ireland

    Barry Smyth

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Peter Brusilovsky Alfred Kobsa Wolfgang Nejdl

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Smyth, B. (2007). Case-Based Recommendation. 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_11

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  • DOI: https://doi.org/10.1007/978-3-540-72079-9_11

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