research-article

User-controllable personalization

Authors:

Peter BrusilovskyAuthors Info & Claims

International Journal of Human-Computer Studies, Volume 78, Issue C

Pages 43 - 67

https://doi.org/10.1016/j.ijhcs.2015.01.007

Published: 01 June 2015 Publication History

Abstract

In this research we investigated the role of user controllability on personalized systems by implementing and studying a novel interactive recommender interface, SetFusion. We examined whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) resulted in increased engagement and a better user experience. The essential contribution of this research stems from the results of a user study (N=40) of controllability in a scenario where users could fuse different recommendation approaches, with the possibility of inspecting and filtering the items recommended. First, we introduce an interactive Venn diagram visualization, which combined with sliders, can provide an efficient visual paradigm for information filtering. Second, we provide a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures. Through the analysis of these metrics, we confirmed results from recent studies, such as the effect of trusting propensity on accepting the recommendations and also unveiled the importance of features such as being a native speaker. Our results present several implications for the design and implementation of user-controllable personalized systems. We explored user-controllable interfaces as extension of traditional-ranked lists.We introduced SetFusion, a controllable interface with sliders and a Venn diagram.We conducted a controlled user study on online conference article recommendation.Our evaluation had three dimensions: users' perception, behavioral and IR metrics.Controllable interface had a positive effect influenced by users' characteristics.

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Cited By

Li SPrasad Majumder BMcAuley J(2024)Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with RationalesACM Transactions on Recommender Systems10.1145/36655023:1(1-20)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3665502
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
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cover image International Journal of Human-Computer Studies

International Journal of Human-Computer Studies Volume 78, Issue C

June 2015

81 pages

ISSN:1071-5819

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Copyright © Elsevier Ltd.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 June 2015

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Cited By

Li SPrasad Majumder BMcAuley J(2024)Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with RationalesACM Transactions on Recommender Systems10.1145/36655023:1(1-20)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3665502
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
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https://dl.acm.org/doi/10.1145/3643709
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Afridi AOlsson T(2023)Review of User Interface-Facilitated Serendipity in Recommender SystemsInternational Journal of Interactive Communication Systems and Technologies10.4018/IJICST.32018012:1(1-19)Online publication date: 17-Mar-2023
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