Article

Trust-aware recommender systems

Authors:

Paolo Massa,

Paolo AvesaniAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 17 - 24

https://doi.org/10.1145/1297231.1297235

Published: 19 October 2007 Publication History

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Abstract

Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.

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Cited By

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Xu XDong HXiang HHu XLi XXia XZhang XQi LDou W(2025)C2lRec: Causal Contrastive Learning for User Cold-start Recommendation with Social VariableACM Transactions on Information Systems10.1145/3711858Online publication date: 9-Jan-2025
https://doi.org/10.1145/3711858
Jafarian BYazdani NSayad Haghighi M(2025)Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entitiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125391260:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125391
Heshmati AMeghdadi MAfsharchi MAhmadian S(2025)SiSRS: Signed social recommender system using deep neural network representation learningExpert Systems with Applications10.1016/j.eswa.2024.125205259(125205)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125205
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Index Terms

Trust-aware recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information

Published In

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

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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Published: 19 October 2007

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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Xu XDong HXiang HHu XLi XXia XZhang XQi LDou W(2025)C2lRec: Causal Contrastive Learning for User Cold-start Recommendation with Social VariableACM Transactions on Information Systems10.1145/3711858Online publication date: 9-Jan-2025
https://doi.org/10.1145/3711858
Jafarian BYazdani NSayad Haghighi M(2025)Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entitiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125391260:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125391
Heshmati AMeghdadi MAfsharchi MAhmadian S(2025)SiSRS: Signed social recommender system using deep neural network representation learningExpert Systems with Applications10.1016/j.eswa.2024.125205259(125205)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125205
El Alaoui DRiffi JSabri AAghoutane BYahyaouy ATairi H(2024)Comparative Study of Filtering Methods for Scientific Research Article RecommendationsBig Data and Cognitive Computing10.3390/bdcc81201908:12(190)Online publication date: 16-Dec-2024
https://doi.org/10.3390/bdcc8120190
Jiang YLi YWei JLiu Y(2024)Research on trust mechanism of supply chain finance under Industrial Internet embedded with blockchainPLOS ONE10.1371/journal.pone.029901119:6(e0299011)Online publication date: 24-Jun-2024
https://doi.org/10.1371/journal.pone.0299011
Huang JOosterhuis HMansoury Mvan Hoof Hde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657749(416-426)Online publication date: 10-Jul-2024
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Abstract

References

Cited By

Index Terms

Recommendations

Trust in recommender systems

Modelling trust networks using resistive circuits for trust-aware recommender systems

Item Reputation-Aware Recommender Systems

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