Evaluating explainable social choice-based aggregation strategies for group recommendation

Francesco Barile*, Tim Draws, Oana Inel, Alisa Rieger, Shabnam Najafian, Amir Ebrahimi Fard, Rishav Hada, Nava Tintarev

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies (N=399 and N=288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.

Original languageEnglish
Number of pages58
JournalUser Modeling and User-Adapted Interaction
Publication statusPublished - 2023


  • Explainable recommender systems
  • Group recommender systems
  • Social choice functions
  • Social choice-based explanations

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