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.