Recurrent Point Processes for Dynamic Review Models
This work addresses the need for better temporal modeling in recommender systems for users and platforms, but appears incremental as it builds on existing methods for temporal representations.
The authors tackled the problem of improving recommender systems by incorporating temporal representations in continuous time using recurrent point processes to model dynamic reviews, aiming to characterize how perception, user interest, and seasonal effects influence review text.
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance. Here, we incorporate temporal representations in continuous time via recurrent point process for a dynamical model of reviews. Our goal is to characterize how changes in perception, user interest and seasonal effects affect review text.