English

Recurrent Point Processes for Dynamic Review Models

Machine Learning 2020-01-16 v2 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.1912.04132,
  title  = {Recurrent Point Processes for Dynamic Review Models},
  author = {Kostadin Cvejoski and Ramses J. Sanchez and Bogdan Georgiev and Jannis Schuecker and Christian Bauckhage and Cesar Ojeda},
  journal= {arXiv preprint arXiv:1912.04132},
  year   = {2020}
}

Comments

Presented at the AAAI 2020 Workshop on Interactive and Conversational Recommendation Systems

R2 v1 2026-06-23T12:40:11.193Z