Generating Natural Language Explanations for Group Recommendations in High Divergence Scenarios

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

In some scenarios, like music or tourism, people often consume items in groups. However, reaching a consensus is difficult as different members of the group may have highly diverging tastes. To keep the rest of the group satisfied, an individual might need to be confronted occasionally with items they do not like. In this context, presenting an explanation of how the system came up with the recommended item(s), may make it easier for users to accept items they might not like for the benefit of the group. This paper presents our progress on proposing improved algorithms for recommending items (for both music and tourism) for a group to consume and an approach for generating natural language explanations. Our future directions include extending the current work by modelling different factors that we need to consider when we generate explanations for groups e.g. size of the group, group members' personality, demographics, and their relationship.
Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Intelligent User Interfaces Companion. IUI 2020
Pages31-32
Number of pages2
ISBN (Electronic)9781450375139
DOIs
Publication statusPublished - 2020
Event25th ACM International Conference on Intelligent User Interfaces, IUI 2020 - Cagliari, Italy
Duration: 17 Mar 202020 Mar 2020
Conference number: 25

Conference

Conference25th ACM International Conference on Intelligent User Interfaces, IUI 2020
Abbreviated titleIUI ’20
CountryItaly
CityCagliari
Period17/03/2020/03/20

Keywords

  • Explanations
  • Group recommendations
  • Human-centered computing user studies
  • Preference aggregation strategies

Cite this

Najafian, S. (2020). Generating Natural Language Explanations for Group Recommendations in High Divergence Scenarios. In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion. IUI 2020 (pp. 31-32) https://doi.org/10.1145/3379336.3381512