Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to incorporate, in a sequential manner, review content knowledge, one is naturally led to dynamical models of text. In the present work, we leverage the known power of reviews to enhance rating predictions, in a way that (i) respects the causality of review generation and (ii) includes, in a bidirectional fashion, the ability of ratings to inform language review models and vice-versa, language representations that help predict ratings end-to-end. Moreover, our representations are time-interval aware and thus yield a continuous-time representation of the dynamics. We provide experiments on real-world datasets and show that our methodology is able to outperform several state-of-the-art models.
Dynamic Review based Recommenders
Dynamic Review based Recommenders.