Fairness and Explainability of Recommender Systems
@FATE_RecSys
Algorithmic Fairness and Biases in RecSys, Explainability in RecSys, Causal and Counterfactual, Transparency in RecSys, Responsible RecSys for Social Good
Here is a list of contributions I made to
@SIGIRConf conducted in collaboration with great colleagues and collaborators in industries (#Amazon, and #DeepMind). Two Long Papers.
Pretty interesting works on the Holistic Evaluation of Generative and Large language models in #IR and #RecSys, Data Generation using LLMs, etc.
Pre-prints coming up, stay tuned... #SIGIR2025 #SIGIR25 @PolibaOfficial@madiator@NikhilMehta@DavenCheung@JulianMcauley
📢🚨 Call for Contributions! #CallForPapers
📅 Deadline: Open now to July 1, 2025
I am extremely pleased to announce our brand-new new special issue with such an incredible team at #ACM_TORS focused on "Generative AI for Recommender Systems" (#GenAI#RecSys#GenRecSys)
Save your papers, or extensions at SIGIR, KDD, UMAP on various topics around #LLMs #LLM4GOOD #Trust #Fairness #Personalization #Evaluation here.
#Perspective and #survey papers are also allowed and welcome in this SI.
Details: https://t.co/1ime0TfAmo
@DavenCheung berndludwig @LinaYao314@AixinSG
📢 Deadline Extended to Sep 5 (AOE)! After receiving many papers!
Working on #LLMs, #GenAI? If your paper did not make it to the #LBR track at @ACMRecSys, do not miss #ROEGEN. We have a senior line-up of speakers and an excellent expecting topic!
👉 https://t.co/HNP1nVCOIG
📚 I am deeply pleased to announce the speaker lineup for #ROEGEN24, the 1st workshop on "Generative Recommender Systems" @ACMRecSys🎉🎉
(1) M. Chen (Principal research scientist at Google Deepmind, USA): "LLMs for Recommendations: A Hybrid Approach"
#LLMs#RecSys#GenerativeAI
Honoured to be a part of this chapter. Let us know what you think and if we have missed any references!
Happy to discuss about evaluation of GenIR systems🤩
🎉I am pleased to share that we will be presenting a #tutorial for #ECAI2024 "Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons & Challenges".
🗓️ 19-24 Oct 2024
📍 Santiago de Compostela
🔗 https://t.co/WvEtXq2NFt
#AI#Ethics#RecSys#LLM
🪅NOW LIVE!🪅
Registration for FAccT 2024 in Rio de Janeiro, Brazil is online! Early Bird rates til April 29! 🐣🌷
🚀https://t.co/NVi6SorTwX
Thank you to our registration chairs @YuexingHao and Sonia Teixeira
Two weeks remain to submit your contributions on #AlgorithmicFairness within #Europe's societal framework @EWAFWorkshop
📅 Submission deadline: March 15, 2024
Location: Germany (July 1-3) @i3mainz
Organized by: M. Cerrato, A. Coronel, and @CorinnaHertweck and J. Alvarez
🚀FAccT's 2024 Call for Tutorials is now online! https://t.co/foBxBo6m1b Deadline March 15 AoE
Led by our Tutorials Chairs, @Jenny_L_Davis@andrewthesmart & @heloisacsp
🚀FAccT's 2024 Call for Tutorials is now online! https://t.co/7OwCeP4eII Deadline March 15 AoE
Led by our Tutorials Chairs, @Jenny_L_Davis@andrewthesmart & @heloisacsp !
🎉 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
Did you know? The "Anywhere on Earth" deadline convention came from IEEE (see: https://t.co/5dYUTgbmKi). It's the home stretch, folks! SIGIR full papers are due Jan 25 AoE time (UTC -12). Y'all got this. (*•̀ᴗ•́*)و ̑̑
"A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News" by @yongfengzhang9 et al.
https://t.co/hKNiFVyhwO
#FairRS#LLMs#ChatGPT
Recent works on #Biases and #Fairness of #RecLLMs:
"Understanding Biases in #ChatGPT-based Rcomemser Systems: provider Fairness, Temporal Stability, and Recency" @yashardel
https://t.co/KTJbGXFLiq
"Fairness in Recommender Systems: Research Landscape and Future Directions" a brand new survey by @yashardel et al. on a timely and important topic is out.
#fairness#recsys
🎉Exciting news!🎉 I am thrilled to announce that our survey on the "fairness of recommender systems" has been accepted by the prestigious #UMUAI journal, the first journal of personalization. https://t.co/SZrCDSWkvU
#fairness#personalization#recsys