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Interactive multi-party critiquing for group recommendation

Authors:

Francesca Guzzi,

Francesco Ricci,

Robin BurkeAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 265 - 268

https://doi.org/10.1145/2043932.2043980

Published: 23 October 2011 Publication History

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Abstract

Group recommender systems (RS) are used to support groups in making common decisions when considering a set of alternatives. Current approaches generate group recommendations based on the users' individual preferences models. We believe that members of a group can reach an agreement more effectively by exchanging proposals suggested by a conventional RS. We propose to use a critiquing RS that has been shown to be effective in single-user recommendation. In the group recommendation context, critiquing allows each user to get new recommendations similar to the proposals made by the other group members and to communicate the rationale behind their own counterproposals. We describe a mobile application implementing the proposed approach and its evaluation in a live user experiment.

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Cited By

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Kraus MKlein SWagner NMinker WAndré E(2024)A Pilot Study on Multi-Party Conversation Strategies for Group RecommendationsProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665569(1-7)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665569
Delic AEmamgholizadeh HNguyen TRicci F(2024)CHARM: a Group Decision Making Support ChatbotCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645220(7-10)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645220
Emamgholizadeh HDelić ARicci FBoratto LGena CMarras MGermanakos PPopescus E(2024)Anticipating Eating Preferences in Group Decision MakingAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664894(329-336)Online publication date: 27-Jun-2024
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Index Terms

Interactive multi-party critiquing for group recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

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

New York, NY, United States

Publication History

Published: 23 October 2011

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RecSys '11

Sponsor:

SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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Kraus MKlein SWagner NMinker WAndré E(2024)A Pilot Study on Multi-Party Conversation Strategies for Group RecommendationsProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665569(1-7)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665569
Delic AEmamgholizadeh HNguyen TRicci F(2024)CHARM: a Group Decision Making Support ChatbotCompanion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645220(7-10)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645220
Emamgholizadeh HDelić ARicci FBoratto LGena CMarras MGermanakos PPopescus E(2024)Anticipating Eating Preferences in Group Decision MakingAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664894(329-336)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664894
Delić AEmamgholizadeh HRicci FMasthoff J(2024)Supporting Group Decision-Making: Insights from a Focus Group StudyProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659538(301-306)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659538
Alves PMartins ANegrão FNovais PAlmeida AMarreiros G(2024)Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systemsExpert Systems with Applications10.1016/j.eswa.2024.124812255(124812)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124812
Delic AEmamgholizadeh HRicci F(2023)CHARM: A Group Recommender ChatBotAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597388(275-282)Online publication date: 26-Jun-2023
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Barile FDelic APeška LSaccardi IUlusoy OVinella F(2023)GMAP 2023: 2nd Workshop on Group Modeling, Adaptation and PersonalizationAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3595628(249-252)Online publication date: 26-Jun-2023
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Emamgholizadeh HBazzanella BMolinari ARicci FBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Single User Group RecommendationsAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3537663(308-313)Online publication date: 4-Jul-2022
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Recommendations

Group recommendations with rank aggregation and collaborative filtering

Item Group Recommendation: A Method Based on Game Theory

Recommending items to group of users using Matrix Factorization based Collaborative Filtering