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Towards a Customization of Rating Scales in Adaptive Systems

  • Federica Cena
  • Fabiana Vernero
  • Cristina Gena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)

Abstract

In web-based adaptive systems, the same rating scales are usually provided to all users for expressing their preferences with respect to various items. It emerged from a user experiment that we recently carried out that different users show different preferences with respect to the rating scales to use in the interface of adaptive systems, given the particular topic they are evaluating. Starting from this finding, we propose to allow users to choose the kind of rating scale they prefer. This approach raises various issues; the most important is that of how an adaptation algorithm can properly deal with values coming from heterogeneous rating scales. We conducted an experiment to investigate how users rate the same object on different rating scales. On the basis of our interpretation of these results, as an example of one possible solution approach, we propose a three-phase normalization process for mapping preferences expressed with different rating scales onto a unique system representation.

Keywords

Recommender System User Rating Adaptive System User Interest Emotional Connotation 
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 2010

Authors and Affiliations

  • Federica Cena
    • 1
  • Fabiana Vernero
    • 1
  • Cristina Gena
    • 1
  1. 1.Dipartimento di InformaticaUniversità di Torino Corso Svizzera 185TorinoItaly

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