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Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study

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  • Chartier, Jean-François
  • Mongeau, Pierre
  • Saint-Charles, Johanne

Abstract

This paper proposes collaborative filtering as a means to predict semantic preferences by combining information on social ties with information on links between actors and semantics. First, the authors present an overview of the most relevant collaborative filtering approaches, showing how they work and how they differ. They then compare three different collaborative filtering algorithms using articles published by New York Times journalists from 2003 to 2005 to predict preferences, where preferences refer to journalists’ inclination to use certain words in their writing. Results show that while preference profile similarities in an actor’s neighbourhood are a good predictor of her semantic preferences, information on her social network adds little to prediction accuracy.

Suggested Citation

  • Chartier, Jean-François & Mongeau, Pierre & Saint-Charles, Johanne, 2020. "Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study," International Journal of Information Management, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:ininma:v:51:y:2020:i:c:s0268401219300866
    DOI: 10.1016/j.ijinfomgt.2019.10.005
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    Cited by:

    1. Anurag Kulshrestha & Venkataraghavan Krishnaswamy & Mayank Sharma, 2023. "A deep learning model for online doctor rating prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1245-1260, August.

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