This book describes research performed in the context of trust/distrust propagation and aggregation, and their use in recommender systems. This is a hot research topic with important implications for various application areas. The main innovative contributions of the work are:
-new bilattice-based model for trust and distrust, allowing for ignorance and inconsistency
-proposals for various propagation and aggregation operators, including the analysis of mathematical properties
-Evaluation of these operators on real data, including a discussion on the data sets and their characteristics.
-A novel approach for identifying controversial items in a recommender system
-An analysis on the utility of including distrust in recommender systems
-Various approaches for trust based recommendations (a.o. base on collaborative filtering), an in depth experimental analysis, and proposal for a hybrid approach
-Analysis of various user types in recommender systems to optimize bootstrapping of cold start users.