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Recommender Systems

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Encyclopedia of Machine Learning and Data Mining

Definition

The goal of a recommender system is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real-world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data available. For example, movie watchers on Netflix frequently provide ratings on a scale of 1 (disliked) to 5 (liked). Such a data source records the quality of interactions between users and items. Additionally, the system may have access to user-specific and item-specific profile attributes such as demographics and product descriptions, respectively. Recommender systems differ in the way they analyze these data sources to develop notions of affinity between users and items, which can be used to identify well-matched pairs. Collaborative Filteringsystems analyze historical...

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Notes

  1. 1.

    Good surveys of the literature in the field can be found in Adomavicius and Tuzhilin (2005), Su (2009), Bell et al. (2009). For extensive empirical comparisons on variations of Collaborative Filtering refer to Breese et al. (1998), Herlocker et al. (1999), and Sarwar et al. (2001).

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Author information

Authors and Affiliations

  1. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

    Prem Melville

  2. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

    Vikas Sindhwani

Authors
  1. Prem Melville
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  2. Vikas Sindhwani
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Melville, P., Sindhwani, V. (2017). Recommender Systems. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_964

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