Understanding information dynamics and their resulting cascades is a central topic in social network analysis. In a recent seminal work, Cheng et al. analyzed multiples cascades on Facebook over several months, and noticed that many of them exhibit a recurring behaviour. They tend to have multiple peaks of popularity, with periods of quiescence in between. In this paper, we propose the first mathematical model that provably explains this interesting phenomenon, besides exhibiting other fundamental properties of information cascades. Our model is simple and shows that it is enough to have a good clustering structure to observe this interesting recurring behaviour with a standard information diffusion model. Furthermore, we complement our theoretical analysis with an experimental evaluation where we show that our model is able to reproduce the observed phenomenon on several social networks.