Mohammadmehdi Naghiaei

Phd Student at USC

[Hossein A. (Saeed) Rahmani @srahmanidashti]

🎉 Excited to share that our paper "A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems" with @naghiaei and @yashardel has been accepted to ACM Transaction on Recommender Systems (@ACM_TORS)

#RecSys @ACMRecSys #ResponsibleAI

[Hossein A. (Saeed) Rahmani @srahmanidashti]

🎉Thrilled to announce that our paper "Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness" has been accepted to #EACL2024 together with @wangxieric, @maliannejadi, @naghiaei, and Emine Yilmaz

#ACL #EACL @aclmeeting #NLProc #ConvAI

[ACM RecSys @ACMRecSys]

Drumroll: #RecSys2023 will be in Singapore! Let’s all meet again there next year.

[Mohammadmehdi Naghiaei @naghiaei]

Interested in context-aware POI recommendation? Come check out the poster session on Sept 21th, I’ll be discussing our paper “Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation” co-authored by @yashardel @srahmanidashti #RecSys2022 @ACMRecSys

[Mohammadmehdi Naghiaei @naghiaei]

Excited to attend my first in-person RecSys conference. @ACMRecSys #RecSys2022

[Hossein A. (Saeed) Rahmani @srahmanidashti]

Excited to share that our short paper titled "Towards Confidence-aware Calibrated Recommendation" with @naghiaei, @maliannejadi, and @NassimSo has been accepted in @cikm2022.

Preprint and codes will be available soon.

@recsystrack @RecSysWiki #RecSys #Calibration #Fairness

[Hossein A. (Saeed) Rahmani @srahmanidashti]

Happy to share that our work has been accepted to @ACMRecSys 2022!

"Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation" w/ Mohammadmehdi Naghiaei (@naghiaei), Ali Tournai (@a_tourani), and Yashar Deldjoo (@yashardel).

#RecSys #Fairness #POI #Bias #Context

[Yashar Deldjoo @yashardel]

2/2 that can find *sweet spots* for optimizing #CPFairness without sacrificing the overall system accuracy. The paper is rich in terms of the literature and provides numerous pointers to research works on #CFairness or #PFairness and shows a traditional gap between them.

[Yashar Deldjoo @yashardel]

1/2 The biases in underlying training data used if left unchecked, could lead to stereotypes, polarization of ideas, or loss of emerging businesses. This *Full* #SIGIR2022 paper proposes a consumer-producer fairness solution… @SIGIRConf @ACMRecSys @FAccTConference @FATE_RecSys

[The IRLab at the University of Amsterdam @irlab_amsterdam]

@naghiaei (U. of Southern California) talking about the generalizability of user-oriented fairness in recommender systems.

[Hossein A. (Saeed) Rahmani @srahmanidashti]

We are excited to share that our paper "CPFair: Personalized Consumer and Producer Fairness
Re-ranking for Recommender Systems" has been accepted at #SIGIR2022! with @naghiaei and @yashardel.

Codes, datasets, and preprint will be available soon.

@SIGIRConf #Fairness #RecSys

[Hossein A. (Saeed) Rahmani @srahmanidashti]

Really happy that our (with @naghiaei, Mahdi Dehghan, and @maliannejadi) paper, "Experiments on Generalizability of User-Oriented Fairness in Recommender Systems", got accepted in the reproducibility track of #SIGIR2022. Codes, datasets, and preprint coming soon.

@SIGIRConf

[The IRLab at the University of Amsterdam @irlab_amsterdam]

🚨 Only a few more hours until we will have the first hybrid edition of SEA. With @le__gab and @naghiaei, talking about reproducibility in IR 🔎.
The event will take place in room SP C0.05 (not SP C0.110, as posted earlier). (1/2)

[Hossein A. (Saeed) Rahmani @srahmanidashti]

Mohammadmehdi (@naghiaei) will present our research study about the generalizability of user-oriented fairness in recommender systems at SEA on 25 March! Don't miss this session.