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An Introduction to Recommender Systems

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Recommender Systems

Abstract

The increasing importance of the Web as a medium for electronic and business transactions has served as a driving force for the development of recommender systems technology. An important catalyst in this regard is the ease with which the Web enables users to provide feedback about their likes or dislikes. For example, consider a scenario of a content provider such as Netflix. In such cases, users are able to easily provide feedback with a simple click of a mouse. A typical methodology to provide feedback is in the form of ratings, in which users select numerical values from a specific evaluation system (e.g., five-star rating system) that specify their likes and dislikes of various items.

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Notes

  1. 1.

    The term “GroupLens” currently refers to the academic group at the University of Minnesota [687] that developed these algorithms. This group continues to work in the area of recommender systems, and has made many pioneering contributions over the years.

  2. 2.

    http://foursquare.com

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    Charu C. Aggarwal

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Aggarwal, C.C. (2016). An Introduction to Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_1

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