# Dynamic Review-based Recommenders

arXiv: https://arxiv.org/abs/2110.14747 · HTML: https://mlsanity.com/paper/2110.14747/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [TransNets: Learning to Transform for Recommendation](https://mlsanity.com/paper/1704.02298/) (2017)
- [Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation](https://mlsanity.com/paper/2001.04346/) (2019)
- [Recurrent Point Review Models](https://mlsanity.com/paper/2012.05684/) (2020)
- [Recurrent Point Processes for Dynamic Review Models](https://mlsanity.com/paper/1912.04132/) (2019)

## Built on by (most cited first, 3 of 3)

- [In-Context Learning of Temporal Point Processes with Foundation Inference Models](https://mlsanity.com/paper/2509.24762/) (2025)
- [The future is different: Large pre-trained language models fail in prediction tasks](https://mlsanity.com/paper/2211.00384/) (2022)
- [On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery](https://mlsanity.com/paper/2510.12640/) (2025)

## Abstract (arXiv)

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].