The pursuit of satisfaction: Affective state in group recommender systems

Judith Masthoff

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

21 Citations (Scopus)


This paper describes three algorithms to model and predict the satisfaction experienced by individuals using a group recommender system which recommends sequences of items. Satisfaction is treated as an affective state. In particular, we model the wearing off of emotion over time and assimilation effects, where the affective state produced by previous items influences the impact on satisfaction of the next item. We compare the algorithms with each other, and investigate the effect of parameter values by comparing the algorithms' predictions with the results of an earlier empirical study. We show a way in which affective state can be used in recommender systems, which is useful for recommendations not only to groups but also to individuals.

Original languageEnglish
Title of host publicationUser Modeling 2005
Subtitle of host publicationProceedings of the 10th International Conference, UM 2005, Edinburgh, Scotland, UK, July 24-29, 2005
EditorsLiliana Ardissono, Paul Brna, Antonija Mitrovic
Place of PublicationBerlin, Germany
Number of pages10
ISBN (Print)3540278850 , 978-3540278856
Publication statusPublished - 18 Jul 2005

Publication series

NameLecture Notes in Artificial Intelligence


  • user modeling
  • group modeling
  • recommender system
  • affective state


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