research-article

Improving Controllability and Predictability of Interactive Recommendation Interfaces for Exploratory Search

Authors:

Antti Kangasrääsiö,

Dorota Glowacka,

Samuel KaskiAuthors Info & Claims

IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces

Pages 247 - 251

https://doi.org/10.1145/2678025.2701371

Published: 18 March 2015 Publication History

Abstract

In exploratory search, when a user directs a search engine using uncertain relevance feedback, usability problems regarding controllability and predictability may arise. One problem is that the user is often modelled as a passive source of relevance information, instead of an active entity trying to steer the system based on evolving information needs. This may cause the user to feel that the response of the system is inconsistent with her steering. Another problem arises due to the sheer size and complexity of the information space, and hence of the system, as it may be difficult for the user to anticipate the consequences of her actions in this complex environment. These problems can be mitigated by interpreting the user's actions as setting a goal for an optimization problem regarding the system state, instead of passive relevance feedback, and by allowing the user to see the predicted effects of an action before committing to it. In this paper, we present an implementation of these improvements in a visual user-controllable search interface. A user study involving exploratory search for scientific literature gives some indication on improvements in task performance, usability, perceived usefulness and user acceptance.

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Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Hoeber OBezanjani AGomes SErfani BNoorally ZShah Y(2024)Visual Keyword/Result Linking: Using Interaction to Dynamically Reveal RelationshipsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638307(66-76)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638307
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Index Terms

Improving Controllability and Predictability of Interactive Recommendation Interfaces for Exploratory Search
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval

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cover image ACM Conferences

IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces

March 2015

480 pages

ISBN:9781450333061

DOI:10.1145/2678025

General Chairs:
Oliver Brdiczka
Vectra Networks, Inc.
,
Polo Chau
Georgia Tech
,
Program Chairs:
Giuseppe Carenini
University of British Columbia
,
Shimei Pan
University of Maryland
,
Per Ola Kristensson
University of Cambridge

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 18 March 2015

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TEKES
Academy of Finland
European Union Seventh Framework Programme

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IUI'15

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IUI'15: IUI'15 20th International Conference on Intelligent User Interfaces

March 29 - April 1, 2015

Georgia, Atlanta, USA

Acceptance Rates

IUI '15 Paper Acceptance Rate 47 of 205 submissions, 23%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

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Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Hoeber OBezanjani AGomes SErfani BNoorally ZShah Y(2024)Visual Keyword/Result Linking: Using Interaction to Dynamically Reveal RelationshipsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638307(66-76)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638307
Radensky MSéguin JLim JOlson KGeiger R(2023)“I Think You Might Like This”: Exploring Effects of Confidence Signal Patterns on Trust in and Reliance on Conversational Recommender SystemsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594043(792-804)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594043
Lee BDowney DLo KWeld D(2023)LIMEADE: From AI Explanations to Advice TakingACM Transactions on Interactive Intelligent Systems10.1145/358934513:4(1-29)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3589345
Radensky MDowney DLo KPopovic ZWeld D(2022)Exploring the Role of Local and Global Explanations in Recommender SystemsExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519795(1-7)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3519795
Zhou ZLi ZZhang YSun L(2022)Transparent-AI Blueprint: Developing a Conceptual Tool to Support the Design of Transparent AI AgentsInternational Journal of Human–Computer Interaction10.1080/10447318.2022.209377338:18-20(1846-1873)Online publication date: 17-Jul-2022
https://doi.org/10.1080/10447318.2022.2093773
Silva NWerneck HSilva TPereira ARocha L(2022)Multi-Armed Bandits in Recommendation SystemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116669197:COnline publication date: 18-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116669
Hoeber OShukla S(2022)A study of visually linked keywords to support exploratory browsing in academic searchJournal of the Association for Information Science and Technology10.1002/asi.2462373:8(1171-1191)Online publication date: 10-Feb-2022
https://doi.org/10.1002/asi.24623
Wilkinson DAlkan ÖLiao QMattetti MVejsbjerg IKnijnenburg BDaly E(2021)Why or Why Not? The Effect of Justification Styles on Chatbot RecommendationsACM Transactions on Information Systems10.1145/344171539:4(1-21)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3441715
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