Dynamic Review-based Recommenders

• Main Authors: Cvejoski, Kostadin, Sanchez, Ramses J, Bauckhage, Christian, Ojeda, Cesar
• Format: Journal Article
• Language: English
• Published: 27-10-2021
• Subjects: Computer Science - Computation and Language; Computer Science - Information Retrieval; Computer Science - Learning
• DOI: 10.48550/arxiv.2110.14747

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].