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Scalable exploration of relevance prospects to support decision making

Verbert, K and Seipp, K and He, C and Parra, D and Wongchokprasitti, C and Brusilovsky, P (2016) Scalable exploration of relevance prospects to support decision making. In: UNSPECIFIED.

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Recent efforts in recommender systems research focus increasingly on human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency, and user control. In this paper, we present a scalable visualisation to interleave the output of several recommender engines with human-generated data, such as user bookmarks and tags. Such a visualisation enables users to explore which recommendations have been bookmarked by like-minded members of the community or marked with a specific relevant tag. Results of a preliminary user study (N =20) indicate that effectiveness and probability of item selection increase when users can explore relations between multiple recommendations and human feedback. In addition, perceived effectiveness and actual effectiveness of the recommendations as well as user trust into the recommendations are higher than a traditional list representation of recommendations.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Verbert, K
Seipp, K
He, C
Parra, D
Wongchokprasitti, Cchw20@pitt.eduCHW20
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 January 2016
Date Type: Publication
Journal or Publication Title: CEUR Workshop Proceedings
Volume: 1679
Page Range: 28 - 35
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1613-0073
Date Deposited: 09 Aug 2017 15:15
Last Modified: 27 Mar 2021 10:55


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