research-article

A Roadmap to User-Controllable Social Exploratory Search

Authors:

Cecilia Di Sciascio,

Peter Brusilovsky,

Christoph Trattner,

Eduardo VeasAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 10, Issue 1

Article No.: 8, Pages 1 - 38

https://doi.org/10.1145/3241382

Published: 30 August 2019 Publication History

Abstract

Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial and error, and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control has been developed to support the exploratory search process. In this work, we present our attempt to increase the power of exploratory search interfaces by using ideas of social search—for instance, leveraging information left by past users of information systems. Social search technologies are highly popular today, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This article presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. This work presents the full series of evaluations conducted to, first, assess the value of the social models in contexts independent to the user interface, in terms of objective and perceived accuracy. Then, in a study with the full-fledged system, we investigated system accuracy and subjective aspects with a structural model revealing that when users actively interacted with all of its control features, the hybrid system outperformed a baseline content-based–only tool and users were more satisfied.

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Cossatin AMauro NArdissono L(2024)Promoting Green Fashion Consumption Through Digital Nudges in Recommender SystemsIEEE Access10.1109/ACCESS.2024.334971012(6812-6829)Online publication date: 2024
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Hu ZMauro NPetrone GArdissono L(2023)Service-based Presentation of Multimodal Information for the Justification of Recommender Systems ResultsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592962(46-53)Online publication date: 18-Jun-2023
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Index Terms

A Roadmap to User-Controllable Social Exploratory Search
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 10, Issue 1

Special Issue on IUI 2018

March 2020

347 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3352585

Editor:
Michelle X. Zhou
Juji, Inc., USA

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Copyright © 2019 ACM.

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Publication History

Published: 30 August 2019

Accepted: 01 October 2018

Revised: 01 September 2018

Received: 01 May 2018

Published in TIIS Volume 10, Issue 1

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Austrian COMET program (funded by FFG)
Marshallplan-Jubiläumsstiftung
MOVING project (Horizon 2020 Framework Programme)

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Cited By

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https://doi.org/10.1515/jigs-2024-0007
Cossatin AMauro NArdissono L(2024)Promoting Green Fashion Consumption Through Digital Nudges in Recommender SystemsIEEE Access10.1109/ACCESS.2024.334971012(6812-6829)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3349710
Hu ZMauro NPetrone GArdissono L(2023)Service-based Presentation of Multimodal Information for the Justification of Recommender Systems ResultsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592962(46-53)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592962
Filippo Hu ZMauro NArdissono L(2023)Image-Based Information Filtering to Compare and Select Items2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00007(1-8)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00007
He CMicallef LSerim BVuong TRuotsalo TJacucci G(2023)Interactive visual facets to support fluid exploratory searchJournal of Visualization10.1007/s12650-022-00865-426:1(211-230)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s12650-022-00865-4
Mauro NHu ZArdissono L(2023)Justification of recommender systems results: a service-based approachUser Modeling and User-Adapted Interaction10.1007/s11257-022-09345-833:3(643-685)Online publication date: 1-Jul-2023
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Hu ZBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Service-aware Recommendation and Justification of ResultsProceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3503252.3534357(341-345)Online publication date: 4-Jul-2022
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Mauro NHu ZArdissono L(2022)Service-Aware Personalized Item RecommendationIEEE Access10.1109/ACCESS.2022.315744210(26715-26729)Online publication date: 2022
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di Sciascio CVeas EBarria-Pineda JCulley CPaternò FOliver NConati CSpano LTintarev N(2020)Understanding the effects of control and transparency in searching as learningProceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377524(498-509)Online publication date: 17-Mar-2020
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