# Recommender System using Reinforcement Learning: A Survey

### Mehrdad Rezaei, Nasseh Tabrizi

#### 2022

#### Abstract

Recommender systems are rapidly becoming an integral part of our daily lives. They play a crucial role in overcoming the overloading problem of information by suggesting and personalizing the recommended items. Collaborative filtering, content-based filtering, and hybrid methods are examples of traditional recommender systems which had been used for straightforward prediction problems. More complex problems can be solved with new methods which are applied to recommender systems, such as reinforcement learning algorithms. Markov decision process and reinforcement learning can take part in solving these problems. Recent developments in applying reinforcement learning methods to recommender systems make it possible to use them in order to solve problems with the massive environment and states. A review of the reinforcement learning recommender system will follow the traditional and reinforcement learning-based methods formulation, their evaluation, challenges, and recommended future work.

Download#### Paper Citation

#### in Harvard Style

Rezaei M. and Tabrizi N. (2022). **Recommender System using Reinforcement Learning: A Survey**. In *Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,* ISBN 978-989-758-584-5, pages 148-159. DOI: 10.5220/0011300300003277

#### in Bibtex Style

@conference{delta22,

author={Mehrdad Rezaei and Nasseh Tabrizi},

title={Recommender System using Reinforcement Learning: A Survey},

booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},

year={2022},

pages={148-159},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0011300300003277},

isbn={978-989-758-584-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,

TI - Recommender System using Reinforcement Learning: A Survey

SN - 978-989-758-584-5

AU - Rezaei M.

AU - Tabrizi N.

PY - 2022

SP - 148

EP - 159

DO - 10.5220/0011300300003277