Our Contributions

The main contributions of this approach can be classified into separate


In this work, we use three real-world well-known check-in datasets Gowalla, Foursquare, and Yelp datasets


We have evaluated datasets with different sample rates used in the offline experiments

Unfairness of Popularity Bias

We have analyzed the impact of Unfairness of Popularity Bias in Point-of-Interest Recommendation.


The proposed approach was robust in terms of providing valuable outcomes.


The full evaluation results are presented in tables below:

Nxt Prv
  • Corr User Average - Gowalla
  • Corr User Average - Yelp
  • Corr User Number - Gowalla
  • Corr User Number - Yelp
  • Corr User Percentage - Gowalla
  • Corr User Percentage - Yelp
  • Long-tail Log - Gowalla
  • Long-tail Log - Yelp
  • Long-tail Normal - Gowalla
  • Long-tail Normal - Yelp
  • User PoI Ratio - Gowalla
  • User PoI Ratio - Yelp

Our Team

We are a diverse group of individuals who bring perspectives to the state-of-the-art projects.

Dr. Yashar Deldjoo

Team Lead

Assistant professor at Polytechnic University of Bari

Hossein A. Rahmani

Researcher & Developer

Visiting Researcher at the University College London

Ali Tourani

Researcher & Developer

Porspective Ph.D. Student at the University of Luxembourg

Mohammadmehdi Naghiaei

Developer & Designer

Ph.D. student at the University of Southern California


You can cite our paper using the reference text below:

author = {Hossein A. Rahmani and Yashar Deldjoo and Ali Tourani and Mohammadmehdi Naghiaei},
title = {The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation},
year = {2022},
url = {https://arxiv.org/abs/2202.13307},
archivePrefix = {arXiv},
primaryClass = {cs.IR},
eprint = {2202.13307}

Contact Us

Have any questions or looking for contribution? We'd love to hear from you.

⚠️ COVID-19 and working from home ⚠️

Currently, we are working remotely! We would be glad if you contact us through email.