Dynamic Review-based Recommenders
This work addresses the challenge of capturing evolving user preferences in recommender systems, which is incremental as it builds on existing review-based methods.
The authors tackled the problem of improving rating predictions in recommender systems by dynamically modeling review content over time, and their method outperformed several state-of-the-art models in experiments on real-world datasets.
Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, 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. Source code for all models can be found at [1].