research-article

A trust-aware recommendation method based on Pareto dominance and confidence concepts

Authors:

Mohammad Mahdi Azadjalal,

Alireza Abdollahpouri,

Mahdi JaliliAuthors Info & Claims

Knowledge-Based Systems, Volume 116, Issue C

Pages 130 - 143

https://doi.org/10.1016/j.knosys.2016.10.025

Published: 15 January 2017 Publication History

Abstract

We proposed a trust-based collaborative filtering method called CPD.Implicit trust statements are identified and used in the recommendation process.A reliability measure is used to evaluate quality of implicit trust statements.Most prominent users are identified using Pareto dominance and confidence concepts.The results show that our method outperformed several state-of-the-art methods. Recommender systems are widely used to provide e-commerce users appropriate items. Collaborative filtering is one of the most successful recommender approaches which recommends items to a given user based on the opinions of his/her like-minded neighbors. However, the user-item ratings matrix, which is used as an input to the recommendation algorithm, is often highly sparse, leading to unreliable predictions. Recent studies demonstrated that information from social networks such as trust statements can be employed to improve accuracy of recommendations. However, there are not explicit trust relationships between most of users in many e-commerce applications. In this manuscript, we propose a method to identify implicit trust statements by applying a specific reliability measure. The Pareto dominance and confidence concepts are used to identify the most prominent users of which opinions are employed in the recommendation process. The proposed recommendation algorithm shows significant improvements in terms of accuracy and coverage measures as compared to the state-of-the-art recommenders.

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Cited By

Deng JLi HGuo JZhang LWang Y(2024)Providing prediction reliability through deep neural networks for recommender systemsComputers and Industrial Engineering10.1016/j.cie.2023.109627185:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.cie.2023.109627
Khaledian NNazari AKhamforoosh KAbualigah LJavaheri D(2023)TrustDLExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120487228:COnline publication date: 15-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120487
Liu JChen YLiu QTekinerdogan B(2023)A similarity-enhanced hybrid group recommendation approach in cloud manufacturing systemsComputers and Industrial Engineering10.1016/j.cie.2023.109128178:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.cie.2023.109128
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Index Terms

A trust-aware recommendation method based on Pareto dominance and confidence concepts
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering

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

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A reliability-based recommendation method to improve trust-aware recommender systems
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Abstract
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Recommender systems are a type of solution to the information overload problem suffered by users of websites that allow the rating of certain items. The collaborative filtering recommender system is considered to be the most successful approach, as it ...

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Information & Contributors

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Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 116, Issue C

January 2017

172 pages

ISSN:0950-7051

Issue’s Table of Contents

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 January 2017

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Cited By

Deng JLi HGuo JZhang LWang Y(2024)Providing prediction reliability through deep neural networks for recommender systemsComputers and Industrial Engineering10.1016/j.cie.2023.109627185:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.cie.2023.109627
Khaledian NNazari AKhamforoosh KAbualigah LJavaheri D(2023)TrustDLExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120487228:COnline publication date: 15-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120487
Liu JChen YLiu QTekinerdogan B(2023)A similarity-enhanced hybrid group recommendation approach in cloud manufacturing systemsComputers and Industrial Engineering10.1016/j.cie.2023.109128178:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.cie.2023.109128
Sun FXu HMeng YLu ZChen SWei QBai C(2023)BERT and Pareto dominance applied to biological strategy decision for bio-inspired designAdvanced Engineering Informatics10.1016/j.aei.2023.10190455:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.aei.2023.101904
Golzardi ESheikhahmadi AAbdollahpouri A(2023)TRTCD: trust route prediction based on trusted community detectionMultimedia Tools and Applications10.1007/s11042-023-15096-482:27(41571-41607)Online publication date: 4-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-023-15096-4
Alhijawi BAwajan AFraihat S(2022)Survey on the Objectives of Recommender Systems: Measures, Solutions, Evaluation Methodology, and New PerspectivesACM Computing Surveys10.1145/352744955:5(1-38)Online publication date: 3-Dec-2022
https://dl.acm.org/doi/10.1145/3527449
Kagita VPujari APadmanabhan VKumar V(2022)Inductive conformal recommender systemKnowledge-Based Systems10.1016/j.knosys.2022.109108250:COnline publication date: 17-Aug-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109108
Li JLin J(2022)A probability distribution detection based hybrid ensemble QoS prediction approachInformation Sciences: an International Journal10.1016/j.ins.2020.01.046519:C(289-305)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.ins.2020.01.046
Rahim ADurrani MGillani SAli ZHasan NKim M(2022)An efficient recommender system algorithm using trust dataThe Journal of Supercomputing10.1007/s11227-021-03991-278:3(3184-3204)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11227-021-03991-2
Chen SZhang CZeng SWang YSu W(2022)A probabilistic linguistic and dual trust network-based user collaborative filtering modelArtificial Intelligence Review10.1007/s10462-022-10175-856:1(429-455)Online publication date: 8-Apr-2022
https://dl.acm.org/doi/10.1007/s10462-022-10175-8
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