This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user's interest similarity. To identify the interest similarity, a user's personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data.

Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this bookvaluable.