Trust- and Distrust-Based Recommendations for Controversial Reviews

Trust, Distrust, and Power

Bilattice-Based Aggregation Operators for Gradual Trust and Distrust

Trust and distrust are two increasingly important metrics in social networks. Since many of these networks are very large, it is only natural that not all users know each other. To this aim, propagation and aggregation operators are often used to estimate (dis)trust relations for users that are not directly connected through the network. In this paper, we introduce bilattice-based aggregation a...

A Framework for Trust and Distrust Networks

In this age of internet and electronic commerce it is becoming increasingly important to have and to manipulate information about the trustworthiness of the content or service providers in order to make informed decisions. This paper explores realistic models of trust and distrust based on partially ordered discrete values and proposes a framework, which is sensitive to local, relative ordering...

Dirichlet PageRank and Ranking Algorithms Based on Trust and Distrust

Motivated by numerous models of representing trust and distrust within a network ranking system, we examine a quantitative vertex ranking with consideration of the influence of a subset of nodes. We propose and analyze a general ranking metric, called Dirichlet PageRank, which gives a ranking of vertices in a subset S of nodes subject to some specified conditions on the vertex boundary of S. In...

Trust- and Location-Based Recommendations for Tourism

Recommender systems in a travel guide suggest touristic sites a user may like. Typically, people are more willing to trust recommendations from people they know. We present a trust-based recommendation service for a mobile tourist guide that uses the notion of directly and indirectly trusted peers. The recommendations combine information about the peers ratings on sights, interpersonal trust an...