Personalised and Dynamic Trust in Social Networks

TL;DR

This paper introduces a novel, personalized, and dynamic trust metric for social networks that enhances recommender systems by accurately capturing indirect trust and social network patterns, validated through analytical, simulation, and empirical methods.

Contribution

It presents a new trust metric that accounts for social network cycles and dynamics, improving upon existing methods for use in recommender systems.

Findings

The trust metric accurately models social trust dynamics.

Simulations confirm the metric's desirable properties.

Empirical tests show improved recommender system performance.

Abstract

We propose a novel trust metric for social networks which is suitable for application in recommender systems. It is personalised and dynamic and allows to compute the indirect trust between two agents which are not neighbours based on the direct trust between agents that are neighbours. In analogy to some personalised versions of PageRank, this metric makes use of the concept of feedback centrality and overcomes some of the limitations of other trust metrics.In particular, it does not neglect cycles and other patterns characterising social networks, as some other algorithms do. In order to apply the metric to recommender systems, we propose a way to make trust dynamic over time. We show by means of analytical approximations and computer simulations that the metric has the desired properties. Finally, we carry out an empirical validation on a dataset crawled from an Internet community and compare the performance of a recommender system using our metric to one using collaborative filtering.