How scales influence user rating behaviour in recommender systems

Federica Cena, Cristina Gena, Pierluigi Grillo, Tsvi Kuflik, Fabiana Vernero, Alan J. Wecker

Research output: Contribution to journalArticlepeer-review


Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.

Original languageEnglish
Pages (from-to)985-1004
Number of pages20
JournalBehaviour and Information Technology
Issue number10
StatePublished - 3 Oct 2017

Bibliographical note

Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.


  • Rating scales
  • human–machine interface
  • recommender system
  • user studies

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Social Sciences (all)
  • Human-Computer Interaction


Dive into the research topics of 'How scales influence user rating behaviour in recommender systems'. Together they form a unique fingerprint.

Cite this