Trust & Distrust-Based Recommendations

When a web application with a built-in recommender offers a social networking component which enables its users to form a trust network, it can generate more personalized recommendations by combining content from the user profiles (ratings) with direct and/or propagated and aggregated information from the trust network. These are the so-called trust-enhanced recommendation systems. As we will explain later on, to be able to provide the users with enough accurate recommendations, the system requires a trust network that consists of a large number of users: the more connections a user has in the trust network, the more recommendations can be generated. Furthermore, more trust connections create more opportunity for qualitative or accurate recommendations. Hence, it is important to trust as many users as possible. However, at the same time, the trust connections you make should reflect your real opinion, otherwise the recommendations will become less accurate. In other words, on the one hand it is advisable to make many trust connections, but on the other hand you need to pay enough attention to which people you really want to trust; in some cases, even distrust can be beneficial for the quality of the recommendations you receive. Consequently, every user needs to find the right balance to get the best out of a trust-based recommendation system.