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Coherence and inconsistencies in rating behavior: estimating the magic barrier of recommender systems
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-2929-0529
Universidad Autónoma de Madrid, Madrid, Spain.
2018 (English)In: User modeling and user-adapted interaction, ISSN 0924-1868, E-ISSN 1573-1391Article in journal (Refereed) Epub ahead of print
Abstract [en]

Recommender Systems have to deal with a wide variety of users and user types that express their preferences in different ways. This difference in user behavior can have a profound impact on the performance of the recommender system. Users receive better (or worse) recommendations depending on the quantity and the quality of the information the system knows about them. Specifically, the inconsistencies in users' preferences impose a lower bound on the error the system may achieve when predicting ratings for one particular user -- this is referred to as the magic barrier.

In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies -- noise. Furthermore, we propose a measure of the consistency of user ratings (rating coherence) that predicts the performance of recommendation methods. More specifically, we show that user coherence is correlated with the magic barrier; we exploit this correlation to discriminate between easy users (those with a lower magic barrier) and difficult ones (those with a higher magic barrier).We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones.We further validate these results by using two public datasets, where the necessary data to identify the magic barrier is not available, in which we obtain similar performance improvements.

Place, publisher, year, edition, pages
2018.
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-15038DOI: 10.1007/s11257-018-9202-0OAI: oai:DiVA.org:his-15038DiVA, id: diva2:1197254
Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-04-17

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Said, Alan

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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