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research-article

TruGRC: : Trust-Aware Group Recommendation with Virtual Coordinators

Authors:

Guangquan ZhangAuthors Info & Claims

Volume 94, Issue C

Pages 224 - 236

https://doi.org/10.1016/j.future.2018.11.030

Published: 01 May 2019 Publication History

Abstract

In recent years, an increase in group activities on websites has led to greater demand for highly-functional group recommender systems. The goal of group recommendation is to capture and distill the preferences of each group member into a single recommendation list that meets the needs of all group members. Existing aggregation functions perform well in harmonious and congruent scenarios, but tend not to generate satisfactory results when group members hold conflicting preferences. Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems. Motivated by these concerns, this paper presents TruGRC, a novel Trust-aware Group Recommendation method with virtual Coordinators, that combines two different aggregation strategies: result aggregation and profile aggregation. As each individual’s preferences are modeled, a virtual user is built as a coordinator to represent the profile aggregation strategy. This coordinator provides a global view of the preferences for all group members by interacting with each user to resolve conflicting preferences. Then, we also model the impact from group members to the virtual coordinator in accordance with personal social influence inferred by trust information on social networks. Group preferences can be easily generated by the average aggregation method under the effect of the virtual coordinator. Experimental results on two benchmark datasets with a range of different group sizes show that TruGRC method has significant improvements compared to other state-of-the-art methods.

Highlights

•

We integrate the result and profile aggregation strategies to improve group recommendation.

•

We introduce a virtual coordinator to create a balanced set of group recommendations.

•

We model trust information and personal influence in group recommender systems.

•

Comprehensive experiments indicate the proposed method outperforms most of baselines.

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Cited By

Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363984
Zhu RLv DYu YZhu RZheng ZBu KLu QWu F(2023)LINet: A Location and Intention-Aware Neural Network for Hotel Group RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583202(779-789)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583202
Ceh-Varela ECao HLauw H(2022)Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral GroupsACM Transactions on Intelligent Systems and Technology10.1145/354280413:6(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3542804
Show More Cited By

Index Terms

TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
2. Information systems

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 94, Issue C

May 2019

957 pages

ISSN:0167-739X

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 May 2019

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Cited By

Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363984
Zhu RLv DYu YZhu RZheng ZBu KLu QWu F(2023)LINet: A Location and Intention-Aware Neural Network for Hotel Group RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583202(779-789)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583202
Ceh-Varela ECao HLauw H(2022)Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral GroupsACM Transactions on Intelligent Systems and Technology10.1145/354280413:6(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3542804
Yannam VKumar JBabu KPatra B(2022)Enhancing the accuracy of group recommendation using slope oneThe Journal of Supercomputing10.1007/s11227-022-04664-479:1(499-540)Online publication date: 11-Jul-2022
https://dl.acm.org/doi/10.1007/s11227-022-04664-4
Yalcin EBilge A(2021)Investigating and counteracting popularity bias in group recommendationsInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10260858:5Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.ipm.2021.102608
Nam L(2021)Towards comprehensive profile aggregation methods for group recommendation based on the latent factor modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115585185:COnline publication date: 15-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115585
Yalcin EBilge A(2021)Novel automatic group identification approaches for group recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114709174:COnline publication date: 15-Jul-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114709
He ZChow CZhang JHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)GAMEProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401064(649-658)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401064

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