Trust For Intelligent Recommendation

Touhid Bhuiyan

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)...