Sunny

SUNNY: A New Algorithm for Trust Inference in Social Networks Using Probabilistic Confidence Models
Ugur Kuter
Department of Computer Science and Institute of Advanced Computer Studies, University of Maryland, College Park, College Park, MD 20742, USA ukuter@cs.umd.edu

Jennifer Golbeck
College of Information Studies, University of Maryland, College Park, College Park, MD 20742, USA jgolbeck@umd.edu

Figure 1: A sample social network with trust values (on a 0-1 scale) as edge labels.

Abstract
In many computing systems, information is produced and processed by many people. Knowing how much a user trusts a source can be very useful for aggregating, filtering, and ordering of information. Furthermore, if trust is used to support decision making, it is important to have an accurate estimate of trust when it is not directly available, as well as a measure of confidence in that estimate. This paper describes a new approach that gives an explicit probabilistic interpretation for confidence in social networks. We describe SUNNY, a new trust inference algorithm that uses a probabilistic sampling technique to estimate our confidence in the trust information from some designated sources. SUNNY computes an estimate of trust based on only those information sources with high confidence estimates. In our experiments, SUNNY produced more accurate trust estimates than the well known trust inference algorithm T IDALT RUST (Golbeck 2005), demonstrating its effectiveness.

available, as well as a measure of confidence in the computed trust value. Given a social network, existing inference algorithms either compute only trust, or conflate trust and confidence, yielding erroneous and ambiguous inferences. As an example, consider the network in Figure 1 which depicts a network with trust values on a 0 to 1 scale. When making a recommendation to A, one may be inclined to decrease the recommended trust value for H because it is derived from nodes who are not trusted much and the path...