Mehdi Afsar, PhD

University of Calgary

H-index: 10

North America-Canada

About Mehdi Afsar, PhD

Mehdi Afsar, PhD, With an exceptional h-index of 10 and a recent h-index of 8 (since 2020), a distinguished researcher at University of Calgary, specializes in the field of Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Reinforcement learning based recommender systems: A survey

Personalized Recommendation Using Reinforcement Learning

Sample Efficiency in Deep Reinforcement Learning based Recommender Systems with Imitation Learning

An Exploration On-demand Article Recommender System for Cancer Patients Information Provisioning

Generating diverse realistic laughter for interactive art

A load balanced recommendation approach

Mehdi Afsar, PhD Information

University	University of Calgary
Position	___
Citations(all)	1071
Citations(since 2020)	730
Cited By	612
hIndex(all)	10
hIndex(since 2020)	8
i10Index(all)	11
i10Index(since 2020)	7
Email	Access Email
University Profile Page	University of Calgary
Google Scholar	View Google Scholar Profile

Mehdi Afsar, PhD Skills & Research Interests

Machine Learning

Top articles of Mehdi Afsar, PhD

Title	Journal	Author(s)	Publication Date
Reinforcement learning based recommender systems: A survey	ACM Computing Surveys	M Mehdi Afsar Trafford Crump Behrouz Far	2022/6/15
Personalized Recommendation Using Reinforcement Learning	PhD Thesis	Mohammad Mehdi Afsar	2022/5
Sample Efficiency in Deep Reinforcement Learning based Recommender Systems with Imitation Learning		M. Mehdi Afsar Trafford Crump Behrouz Far	2022
An Exploration On-demand Article Recommender System for Cancer Patients Information Provisioning	The International FLAIRS Conference Proceedings	Mohammad Mehdi Afsar Trafford Crump Behrouz Far	2021/4/18
Generating diverse realistic laughter for interactive art	arXiv preprint arXiv:2111.03146	M Mehdi Afsar Eric Park Étienne Paquette Gauthier Gidel Kory W Mathewson ...	2021/11/4
A load balanced recommendation approach	arXiv preprint arXiv:2105.09981	Mehdi Afsar Trafford Crump Behrouz Far	2021/5/20