Recommender systems are one of the recent inventions to deal with the ever-growing information
overload in relation to the selection of goods and services in a global economy. Collaborative
Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends
items to a target user based on the preferences of a set of similar users known as the
neighbors generated from a database made up of the preferences of past users. In the absence
of these ratings trust between the users could be used to choose the neighbor for
recommendation making. Better recommendations can be achieved using an inferred trust network
which mimics the real world friend of a friend recommendations. To extend the boundaries of the
neighbor an effective trust inference technique is required. 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.
