Article

Hybrid critiquing-based recommender systems

Authors:

Pearl PuAuthors Info & Claims

IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces

Pages 22 - 31

https://doi.org/10.1145/1216295.1216308

Published: 28 January 2007 Publication History

Abstract

We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such self-motivated critiquing support enables users to achieve a higher level of decision accuracy while consuming less cognitive effort. In addition, users expressed higher subjective opinions of the hybrid critiquing interface than the interface simply providing system-proposed critiques, and they would more likely return to it for future use.

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Cited By

Rana ASanner SBouadjenek MDi Carlantonio RFarmaner G(2023)User Experience and the Role of Personalization in Critiquing-Based Conversational RecommendationACM Transactions on the Web10.1145/359749918:4(1-21)Online publication date: 18-May-2023
https://dl.acm.org/doi/10.1145/3597499
Petrescu DAntognini DFaltings B(2021)Multi-Step Critiquing User Interface for Recommender SystemsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478886(760-763)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3478886
Jannach DManzoor ACai WChen L(2021)A Survey on Conversational Recommender SystemsACM Computing Surveys10.1145/345315454:5(1-36)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3453154
Show More Cited By

Index Terms

Hybrid critiquing-based recommender systems
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
    2. Interaction paradigms
      1. Graphical user interfaces
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design

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Critiquing-based recommender systems elicit users' feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single ...
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The critiquing-based recommender system mainly aims to guide users to make an accurate and confident decision, while requiring them to consume a low level of effort. We have previously found that the hybrid critiquing system of combining the strengths ...

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Published In

cover image ACM Conferences

IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces

January 2007

388 pages

ISBN:1595934812

DOI:10.1145/1216295

General Chairs:
David Chin
University of Hawaii, USA
,
Michelle Zhou
IBM Watson Research Center, USA
,
Program Chairs:
Tessa Lau
Almaden Research, USA
,
Angel Puerta
RedWhale Software, USA

Copyright © 2007 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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Publication History

Published: 28 January 2007

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IUI07

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IUI07: 12th International Conference on Intelligent User Interfaces

January 28 - 31, 2007

Hawaii, Honolulu, USA

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

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Cited By

Rana ASanner SBouadjenek MDi Carlantonio RFarmaner G(2023)User Experience and the Role of Personalization in Critiquing-Based Conversational RecommendationACM Transactions on the Web10.1145/359749918:4(1-21)Online publication date: 18-May-2023
https://dl.acm.org/doi/10.1145/3597499
Petrescu DAntognini DFaltings B(2021)Multi-Step Critiquing User Interface for Recommender SystemsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478886(760-763)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3478886
Jannach DManzoor ACai WChen L(2021)A Survey on Conversational Recommender SystemsACM Computing Surveys10.1145/345315454:5(1-36)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3453154
Viappiani PBoutilier C(2020)On the equivalence of optimal recommendation sets and myopically optimal query setsArtificial Intelligence10.1016/j.artint.2020.103328(103328)Online publication date: May-2020
https://doi.org/10.1016/j.artint.2020.103328
Jin YCai WChen LHtun NVerbert KZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational InteractionProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357923(951-960)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357923
Chen LWang FPu PDíaz Agudo BGoel A(2017)Investigating users’ eye movement behavior in critiquing-based recommender systemsAI Communications10.3233/AIC-17073730:3-4(207-222)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/AIC-170737
Benito‐Picazo FEnciso MRossi CGuevara A(2017)Enhancing the conversational process by using a logical closure operator in phenotypes implicationsMathematical Methods in the Applied Sciences10.1002/mma.433841:3(1089-1100)Online publication date: 16-Feb-2017
https://doi.org/10.1002/mma.4338
Huaiqing HHongrui DHaohan L(2016)Overview and Investigation of the Visualization Methods in Recommendation Systems2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN.2016.123(601-605)Online publication date: Dec-2016
https://doi.org/10.1109/CICN.2016.123
Kaššák OKompan MBieliková M(2016)Personalized hybrid recommendation for group of usersInformation Processing and Management: an International Journal10.1016/j.ipm.2015.10.00152:3(459-477)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.ipm.2015.10.001
Chen LWang FWu W(2016)Inferring Users’ Critiquing Feedback on Recommendations from Eye MovementsCase-Based Reasoning Research and Development10.1007/978-3-319-47096-2_5(62-76)Online publication date: 29-Sep-2016
https://doi.org/10.1007/978-3-319-47096-2_5
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