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Recommendation Systems in-the-Small

  • Laura Inozemtseva
  • Reid Holmes
  • Robert J. Walker
Chapter

Abstract

Many recommendation systems rely on data mining to produce their recommendations. While data mining is useful, it can have significant implications for the infrastructure needed to support and to maintain an RSSE; moreover, it can be computationally expensive. This chapter examines recommendation systems in-the-small (RITSs), which do not rely on data mining. Instead, they take small amounts of data from the developer’s local context as input and use heuristics to generate recommendations from that data. We provide an overview of the burdens imposed by data mining and how these can be avoided by a RITS through the use of heuristics. Several examples drawn from the literature illustrate the applications and designs of RITSs. We provide an introduction to the development of the heuristics typically needed by a RITS. We discuss the general limitations of RITSs.

Keywords

Data Mining Recommendation System Application Programming Interface Integrate Development Environment Cold Start Problem 
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-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Laura Inozemtseva
    • 1
  • Reid Holmes
    • 1
  • Robert J. Walker
    • 2
  1. 1.David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

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