research-article

Trust Prediction via Matrix Factorisation

Author:

Pasquale De MeoAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 19, Issue 4

Article No.: 44, Pages 1 - 20

https://doi.org/10.1145/3323163

Published: 19 September 2019 Publication History

Abstract

In this article, we propose the PTP-MF (Pairwise Trust Prediction through Matrix Factorisation) algorithm, an approach to predicting the intensity of trust and distrust relations in Online Social Networks (OSNs).

Our algorithm maps each OSN user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and it computes the trust a user i places in a user j as the dot product of trustor profile of i and the trustee profile of j. The PTP-MF algorithm incorporates also biases in trustor and trustee behaviour to make more accurate predictions.

Experiments on four real-life datasets indicate that the PTP-MF algorithm significantly outperforms other methods in accuracy and it showcases a high scalability.

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Index Terms

Trust Prediction via Matrix Factorisation
1. Human-centered computing
  1. Collaborative and social computing

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 19, Issue 4

Special Section on Trust and AI and Regular Papers

November 2019

201 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3362102

Editor:
Ling Liu
Ling Liu Georgia Institute of Technology, USA

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Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 19 September 2019

Accepted: 01 March 2019

Revised: 01 February 2019

Received: 01 November 2018

Published in TOIT Volume 19, Issue 4

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INdAM?GNCS Project 2019 “Innovative methods for the solution of medical and biological big data”

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Yuan YChen LYang J(2023)A Multidimensional Model for Recommendation Systems Based on Classification and EntropyElectronics10.3390/electronics1202040212:2(402)Online publication date: 12-Jan-2023
https://doi.org/10.3390/electronics12020402
Alabduljabbar R(2023)Matrix Factorization Collaborative-Based Recommender System for Riyadh Restaurants: Leveraging Machine Learning to Enhance Consumer ChoiceApplied Sciences10.3390/app1317957413:17(9574)Online publication date: 24-Aug-2023
https://doi.org/10.3390/app13179574
Ren QQin JShao JSong X(2023)RSII: A Recommendation Algorithm That Simulates the Generation of Target Review Semantics and Fuses ID InformationApplied Sciences10.3390/app1306394213:6(3942)Online publication date: 20-Mar-2023
https://doi.org/10.3390/app13063942
Wang JBatjargal BMaeda AKawagoe KAkama R(2023)Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital ArchivesApplied Sciences10.3390/app1304243513:4(2435)Online publication date: 14-Feb-2023
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