Documents

DOI

In some scenarios, like music, people often consume items in groups. However, reaching a consensus is difficult, and often compromises need to be made. Such compromises can potentially help users expand their tastes. They can also lead to outright rejection of the recommended items. One way to avoid this is to explain recommendations that are surprising, or even expected to be disliked, by an individual user. This paper presents an approach for generating explanations for groups. We propose algorithms for selecting a sequence of songs for a group to consume. These algorithms consider consensus but have different trade-offs. Next, using these algorithms we generated explanations in a layered evaluation using synthetic data. We studied the influence of these explanations in structured interviews with users (n=16) on user satisfaction
Original languageEnglish
Title of host publicationUMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY
PublisherAssociation for Computer Machinery
Pages245-250
Number of pages6
ISBN (Print)978-1-4503-5784-5
DOIs
Publication statusPublished - 2018
EventUMAP 2018 : The 26th Conference on User Modeling, Adaptation and Personalization - Singapore, Singapore
Duration: 8 Jul 201811 Jul 2018
Conference number: 26

Conference

ConferenceUMAP 2018
Abbreviated titleUMAP '18
CountrySingapore
CitySingapore
Period8/07/1811/07/18

    Research areas

  • Explanations, Group recommendation, Sequences

ID: 45182960