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An Interactive and Interpretable Interface for Diversity in Recommender Systems

Tsai, Chun-Hua (2017) An Interactive and Interpretable Interface for Diversity in Recommender Systems. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces Companion, 13-16 Mar 2017, Limassol, Cyprus.

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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.


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Details

Item Type: Conference or Workshop Item (Poster)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Tsai, Chun-Huacht77@pitt.educht770000-0001-9188-0362
Date: 13 March 2017
Date Type: Publication
Series Name: IUI '17 Companion
Publisher: ACM
Place of Publication: New York, NY, USA
Page Range: pp. 225-228
Event Title: Proceedings of the 22nd International Conference on Intelligent User Interfaces Companion
Event Dates: 13-16 Mar 2017
Event Type: Conference
Refereed: Yes
Uncontrolled Keywords: HCI, diversity, recommender system
Official URL: http://doi.acm.org/10.1145/3030024.3038292
Date Deposited: 09 Aug 2017 15:19
Last Modified: 09 Aug 2017 15:19
URI: http://d-scholarship.pitt.edu/id/eprint/32807

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