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Trust plays a crucial role for online users who seek reliable information. However, in reality, user-specified trust relations are very sparse, i.e., a tiny number of pairs of users with trust relations are buried in a disproportionately large number of pairs without trust relations, making trust prediction a daunting task. As an important social concept, however, trust has received growing attention and interest. Social theories are developed for understanding trust. Homophily is one of the most important theories that explain why trust relations are established. Exploiting the homophily effect for trust prediction provides challenges and opportunities. In this paper, we embark on the challenges to investigate the trust prediction problem with the homophily effect. First, we delineate how it differs from existing approaches to trust prediction in an unsupervised setting. Next, we formulate the new trust prediction problem into an optimization problem integrated with homophily, empirically evaluate our approach on two datasets from real-world product review sites, and compare with representative algorithms to gain a deep understanding of the role of homophily in trust prediction.

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