Trust prediction with propagation and similarity regularization

Xiaoming Zheng*, Yan Wang, Mehmet A. Orgun, Youliang Zhong, Guanfeng Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

14 Citations (Scopus)

Abstract

Online social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decisionmaking of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modeling, and process them separately and differently. In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendency-reduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users' rating habits. In the end. The missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a real-world dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches.

Original languageEnglish
Title of host publicationProceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
Place of PublicationQuébec, Canada
PublisherAI Access Foundation
Pages237-243
Number of pages7
Volume1
ISBN (Electronic)9781577356776
Publication statusPublished - 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period27/07/1431/07/14

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    Zheng, X., Wang, Y., Orgun, M. A., Zhong, Y., & Liu, G. (2014). Trust prediction with propagation and similarity regularization. In Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence (Vol. 1, pp. 237-243). Québec, Canada: AI Access Foundation.