An interactive and interpretable interface for diversity in recommender systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Offering diversity in the output of a recommender system is an active research question. Most of the current approaches focus on Top-N optimization, which results in poor user insight and accuracy trade-off. However, little is known about how an interactive interface can help with this issue. This pilot study shows that a multidimensional visualization promotes diversity among the recommended items. This finding motivated future work to provide diversity in recommender system by visualizing multivariate data through an interpretable and interactive interface. Copyright held by the owner/author(s).

Original languageEnglish (US)
Title of host publicationIUI 2017 - Companion of the 22nd International Conference on Intelligent User Interfaces
PublisherAssociation for Computing Machinery
Pages225-228
Number of pages4
ISBN (Electronic)9781450348935
DOIs
StatePublished - Mar 7 2017
Event22nd International Conference on Intelligent User Interfaces, IUI 2017 - Limassol, Cyprus
Duration: Mar 13 2017Mar 16 2017

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI

Conference

Conference22nd International Conference on Intelligent User Interfaces, IUI 2017
CountryCyprus
CityLimassol
Period3/13/173/16/17

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction

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  • Cite this

    Tsai, C. H. (2017). An interactive and interpretable interface for diversity in recommender systems. In IUI 2017 - Companion of the 22nd International Conference on Intelligent User Interfaces (pp. 225-228). (International Conference on Intelligent User Interfaces, Proceedings IUI). Association for Computing Machinery. https://doi.org/10.1145/3030024.3038292