Recurrent Point Review Models

TL;DR

This paper introduces recurrent point review models that integrate temporal review data and content representations to improve language modeling and user preference prediction over time in recommender systems.

Contribution

It develops novel recurrent and temporal convolution models that incorporate review content and temporal dynamics for enhanced predictive performance.

Findings

Improved language prediction accuracy over time.

Enhanced user preference modeling in recommender systems.

Effective integration of review content and temporal information.

Abstract

Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal information and model how to review data changes with time. Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how business or service reviews are received in time, to generate instantaneous language models with improved prediction capabilities. Simultaneously, our methodologies enhance the predictive power of our point process models by incorporating summarized review content representations. We provide recurrent network and temporal convolution solutions for modeling the review content. We deploy our methodologies in the context of recommender systems, effectively characterizing the change in preference and taste of users as time evolves. Source code is available at [1].