Trust-based Recommendations through Triadic Closure

Information filtering and recommender systems exploit various techniques to deal with the information overload problem. One popular technique for making recommendations is based on trust statements between users in a social network. Yet explicit trust statements are usually very sparse leading to the need for expanding the trust networks by inferring new trust relationships. This study proposes a novel model to infer new trusted neighbors and combines it with existing methods to achieve a fully expanded trust network. Experimental results demonstrate that the proposed method outperforms the existing methods.