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IntRS@RecSys, Date: 2016/09/16 - 2016/09/16, Location: Boston, USA

Publication date: 2016-09-06
Volume: 1679 Pages: 28 - 35
Publisher: CEUR Workshop Proceedings

Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2016)


Verbert, Katrien
Seipp, Karsten ; He, Chen ; Parra, Denis ; Wongchokprasitti, Chirayu ; Brusilovsky, Peter ; Brusilovsky, Peter ; Felfernig, Alexander ; Lops, Pasquale ; O'Donovan, John ; Semeraro, Giovanni ; Tintarev, Nava ; Willemsen, Martijn C


recommender systems, HCI, visualisation


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.