Computational Trust Models and Machine Learning

This book explores trust modeling in computer science, emphasizing machine learning's role in enhancing reputation systems and decision support.

The book Computational Trust Models and Machine Learning offers a comprehensive introduction to the concept of trust and its significance across various domains of computer science. It delves into multi-agent systems, online social networks, and communication systems, highlighting the challenges that traditional trust modeling approaches face. By identifying these challenges, the book sets the stage for exploring innovative solutions that leverage modern technology.

In Computational Trust Models and Machine Learning, readers will discover how reputation-based systems play a crucial role in assessing trust within diverse online communities. The author elaborates on the application of machine learning techniques to create robust reputation systems that can adapt and improve over time. Additionally, the book examines two distinct methods for evaluating the credibility of resources: one that operates with implicit human involvement and another that actively incorporates human input.

Moreover, the text discusses the importance of computational trust models in facilitating decision support and enhancing collaborative filtering-based trust-aware recommendation systems. It also presents a framework for translating trust modeling challenges into learning problems, while addressing the objectivity of human feedback and the necessity of filtering out outlier opinions. Overall, Computational Trust Models and Machine Learning effectively illustrates how advanced machine learning techniques can enhance the accuracy of trust assessments in various applications.