Sequential group recommendations based on satisfaction and disagreement scores

Maria Stratigi, Evaggelia Pitoura, Jyrki Nummenmaa, Kostas Stefanidis

Research output: Contribution to journalArticleScientificpeer-review

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Recently, group recommendations have gained much attention. Nevertheless, most approaches consider only one round of recommendations. However, in a real-life scenario, it is expected that the history of previous recommendations is exploited to tailor the recommendations towards meeting the needs of the group members. Such history should include not only which items the system suggested, but also the reaction of the members to these items. This work introduces the problem of sequential group recommendations, by exploiting the concept of satisfaction and disagreement. Satisfaction describes how well the group received the suggested items. Disagreement describes the satisfaction bias among the group members. We utilize these concepts in three new aggregation methods, SDAA, SIAA and Average+, designed to address the specific challenges introduced by sequential group recommendations. We experimentally show the effectiveness of our methods using big real datasets for both stable and ephemeral groups.

Original languageEnglish
Pages (from-to)227–254
JournalJournal of Intelligent Information Systems
Issue number2
Early online date14 Jul 2021
Publication statusPublished - 2022
Publication typeA1 Journal article-refereed


  • Group recommendations
  • Sequential recommendations
  • User disagreement
  • User satisfaction

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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