research-article

TrustDL: : Use of trust-based dictionary learning to facilitate recommendation in social networks

Authors:

Navid Khaledian,

Keyhan Khamforoosh,

Laith Abualigah,

Danial JavaheriAuthors Info & Claims

Volume 228, Issue C

https://doi.org/10.1016/j.eswa.2023.120487

Published: 15 October 2023 Publication History

Abstract

Collaborative filtering (CF) is a widely applied method to perform recommendation tasks in a wide range of domains and applications. Dictionary learning (DL) models, which are highly important in CF-based recommender systems (RSs), are well represented by rating matrices. However, these methods alone do not resolve the cold start and data sparsity issues in RSs. We observed a significant improvement in rating results by adding trust information on the social network. For that purpose, we proposed a new dictionary learning technique based on trust information, called TrustDL, where the social network data were employed in the process of recommendation based on structural details on the trusted network. TrustDL sought to integrate the sources of information, including trust statements and ratings, into the recommendation model to mitigate both problems of cold start and data sparsity. It conducted dictionary learning and trust embedding simultaneously to predict unknown rating values. In this paper, the dictionary learning technique was integrated into rating learning, along with the trust consistency regularization term designed to offer a more accurate understanding of the feature representation. Moreover, partially identical trust embedding was developed, where users with similar rating sets could cluster together, and those with similar rating sets could be represented collaboratively. The proposed strategy appears significantly beneficial based on experiments conducted on four frequently used datasets: Epinions, Ciao, FilmTrust, and Flixster.

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Index Terms

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks
1. Human-centered computing

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 228, Issue C

Oct 2023

1608 pages

ISSN:0957-4174

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 October 2023

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Cited By

Yu XLi WZhang CWang JZhao YLiu FPan QLiu HDing JChen D(2024)Time-aware multi-behavior graph network model for complex group behavior predictionInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10366661:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103666
Chen SZhou S(2024)An extended trust and distrust network-based dual fuzzy recommendation model and its application based on user-generated contentExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123360248:COnline publication date: 15-Aug-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123360
Zhong MXiong WLi DChen KZhang L(2024)MaskDUFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121995238:PCOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121995
Bakhtiari NRafighi MAhsan R(2024)A trust management system for fog computing using improved genetic algorithmThe Journal of Supercomputing10.1007/s11227-024-06271-x80:14(20923-20955)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-06271-x
Abbasi SKhaledian NRahmani A(2024)Trust management in the internet of vehicles: a systematic literature review of blockchain integrationInternational Journal of Information Security10.1007/s10207-024-00878-023:4(3065-3088)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10207-024-00878-0
Cai RLu RChen WHao Z(2024)Counterfactual contextual bandit for recommendation under delayed feedbackNeural Computing and Applications10.1007/s00521-024-09800-036:23(14599-14613)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09800-0
Zhang YGu X(2024)Enhancing user and item representation with collaborative signals for KG-based recommendationNeural Computing and Applications10.1007/s00521-024-09419-136:12(6681-6699)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00521-024-09419-1

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