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EnglishIn 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.
| Titolo: | Trust Prediction via Matrix Factorisation |
| Autori: | DE MEO, Pasquale (Corresponding) |
| Data di pubblicazione: | 2019 |
| Rivista: | |
| 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. |
| Handle: | http://hdl.handle.net/11570/3148018 |
| Appare nelle tipologie: | 14.a.1 Articolo su rivista |
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http://hdl.handle.net/11570/3148018
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