Trust-based recommender systems emerged as a solution to different limitations of traditional recommender systems. These systems rely on the assumption that users will adopt the preferences of users they deem trustworthy in an online social setting. However, most trust-based recommender systems consider trust to be a static notion, thereby disregarding crucial dynamic factors that influence the value of trust between users and the performance of the recommender system. In this work, we intend to address several challenges regarding the dynamics of trust within a trust-based recommender system. These issues include the temporal evolution of trust between users and change detection and prediction in users’ interactions. By exploring the factors that influence the evolution of human trust, a complex and abstract concept, this work will contribute to a better understanding of how trust operates in recommender systems.