article

Managing uncertainty in group recommending processes

Authors:

Luis M. Campos,

Juan M. Fernández-Luna,

Miguel A. Rueda-MoralesAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 19, Issue 3

Pages 207 - 242

https://doi.org/10.1007/s11257-008-9061-1

Published: 01 August 2009 Publication History

Abstract

While the problem of building recommender systems has attracted considerable attention in recent years, most recommender systems are designed for recommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group recommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes.

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Cited By

Cruz JOliveira ASilva DDurão F(2022)GRSPOI: A Point-of-Interest Recommender Systems for Groups Using DiversificationProceedings of the XVIII Brazilian Symposium on Information Systems10.1145/3535511.3535519(1-8)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3535511.3535519
Liao GDeng XWan CLiu X(2022)Group event recommendation based on graph multi-head attention network combining explicit and implicit informationInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10279759:2Online publication date: 9-May-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102797
Deng XLiao GZeng Y(2022)Group event recommendation based on a heterogeneous attribute graph considering long- and short- term preferencesJournal of Intelligent Information Systems10.1007/s10844-022-00758-w61:1(271-297)Online publication date: 2-Nov-2022
https://dl.acm.org/doi/10.1007/s10844-022-00758-w
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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 19, Issue 3

August 2009

138 pages

ISSN:0924-1868

Issue’s Table of Contents

Copyright © Copyright © 2009 Springer Science+Business Media B.V.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2009

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Cited By

Cruz JOliveira ASilva DDurão F(2022)GRSPOI: A Point-of-Interest Recommender Systems for Groups Using DiversificationProceedings of the XVIII Brazilian Symposium on Information Systems10.1145/3535511.3535519(1-8)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3535511.3535519
Liao GDeng XWan CLiu X(2022)Group event recommendation based on graph multi-head attention network combining explicit and implicit informationInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10279759:2Online publication date: 9-May-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102797
Deng XLiao GZeng Y(2022)Group event recommendation based on a heterogeneous attribute graph considering long- and short- term preferencesJournal of Intelligent Information Systems10.1007/s10844-022-00758-w61:1(271-297)Online publication date: 2-Nov-2022
https://dl.acm.org/doi/10.1007/s10844-022-00758-w
Jia JYao YLei ZLiu P(2021)Dynamic Group Recommendation Algorithm Based on Member Activity LevelScientific Programming10.1155/2021/19691182021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/1969118
Liao GDeng XHuang XWan C(2020)FHAN: Feature-Level Hierarchical Attention Network for Group Event RecommendationWeb and Big Data10.1007/978-3-030-60259-8_35(478-492)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-60259-8_35
Herzog DWörndl WBogers TSaid ABrusilovsky PTikk D(2019)User-centered evaluation of strategies for recommending sequences of points of interest to groupsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346988(96-100)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3346988
Kim HSaddik A(2015)A stochastic approach to group recommendations in social media systemsInformation Systems10.1016/j.is.2014.10.00250:C(76-93)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1016/j.is.2014.10.002
Zapata AMenéndez VPrieto MRomero C(2015)Evaluation and selection of group recommendation strategies for collaborative searching of learning objectsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2014.12.00276:C(22-39)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.ijhcs.2014.12.002
Langseth HNielsen T(2015)Scalable learning of probabilistic latent models for collaborative filteringDecision Support Systems10.1016/j.dss.2015.03.00674:C(1-11)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1016/j.dss.2015.03.006
Chaney AGartrell MHofman JGuiver JKoenigstein NKohli PPaquet UOlivier PWright PBartindale TObrist MCesar PBasapur S(2014)A large-scale exploration of group viewing patternsProceedings of the ACM International Conference on Interactive Experiences for TV and Online Video10.1145/2602299.2602309(31-38)Online publication date: 25-Jun-2014
https://dl.acm.org/doi/10.1145/2602299.2602309
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