Top Tweets for #RecSys22
Watch a technical walkthrough implementation of the four stages of recommender systems—retrieval, filtering, scoring, and ordering—a pattern that we feel covers the majority of recommender systems deployed today. @ACMRecSys #RecSys22 #RecSys2023 https://t.co/ZHEvTPxyJ9
Watch a technical walkthrough implementation of the four stages of recommender systems—retrieval, filtering, scoring, and ordering—a pattern that we feel covers the majority of recommender systems deployed today. @ACMRecSys #RecSys22 #RecSys2023 https://t.co/euE3pvh5LD
Hello, network,
I completed a wrap-up of my notes on the @ACMRecSys #recsys22 conference. As usual, an incredible conference with plenty of inspiration for future works.
https://t.co/DbUgJWGHZk
In this blog, we present a four-stage design pattern that explains what recommender systems (not just models) look like in production. @ACMRecsys #RecSys2022 #RecSys22 https://t.co/NO4Ag9faCW
Do you want to bridge the gap between building recommender models and a production system that serves recommendations? In this blog, we present a four-stage design pattern that explains what recommender systems look like in production. @ACMRecSys #RecSys22 https://t.co/7rX8D1XkDY
#RecSys22 was wonderful this year, papers were quite wide and interesting and the quality of workshops have improved alot compared to the last years. Many of papers presented in the workshops have the potential to be part of main proceedings. Thanks and kudos to the organizers!
Amanda Arid is presenting on personalization, bossiness and the cost of fairness @FAccTRec. Find the paper here:
https://t.co/a8Z39ZnCUn
@ACMRecSys #RecSys22
Karlijn Dinnissen is presenting a stakeholder-centered view on fairness in music recommender systems. Find the paper here: https://t.co/wHdTn05xWZ
@FAccTRec @ACMRecSys #RecSys22
Thomas Kolb is presenting the role of bias in news recommendations in the perception of the COVID-19 pandemic. Find the paper here: https://t.co/ArQJtMd4yk
@FAccTRec @ACMRecSys #RecSys22
On the final day of #RecSys22, attending this future looking workshop on future of work - RecWork @ACMRecSys
Random is not fair, packett-luce sampling is better, a work by #spotify at @FAccTRec workshop #recsys22
A talk focoused on producer fairness.
#fairness #producer #random #recsys2022
#recsys2022 @facctrec workshop: Session 2 (on-line)
* Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews
Happy to join #fashionXRecSys workshop at #recsys22.
Check out our paper at RecSys: https://t.co/FxgLhENlKD
And the survey we ahev recently written on the topic with a group of experts from #academia and #Amazon: https://t.co/q7ZG4WBEtx
#fashion #recsys #adversarial #attack
1/3 A truly thrilling announcement 👇
"A Review of Modern Fashion Recommender Systems" is now available at: https://t.co/y76uP0bogg
Co-authored with A.Ramisa (Amazon) @JulianMcauley @AtenaNazary
@abellogin @TommasoDiNoia
#fashion #recsys #search
For the 2nd time super proud of my students @R_Verachtert and @lienmichiels, this time for the best paper award at the #recsys22 #perspectives22 workshop! A very nice paper explaining why you need to take the training time window into account when evaluating a recommender system
Best Paper Award at the #Perspectives22 workshop at #RecSys22 with @R_Verachtert and @goethals 😍 We’ve been applying the principles explained in the paper in our production experiments at @Froomle for years, with impressive results!
Best Paper Award at the #Perspectives22 workshop at #RecSys22 with @R_Verachtert and @goethals 😍 We’ve been applying the principles explained in the paper in our production experiments at @Froomle for years, with impressive results!
Watch a technical walkthrough implementation of the four stages of recommender systems—retrieval, filtering, scoring, and ordering—a pattern that we feel covers the majority of recommender systems deployed today. @ACMRecSys #RecSys22 #RecSys2022 https://t.co/HyDnDdREJa
In this paper, the NVIDIA team presents its solution from the @ACMRecSys Challenge for building an effective session-based #recommender using NVIDIA AI for more refined, robust, and accurate recommendations. #RecSys22 #RecSys2022 https://t.co/kbcSZft2lV
My paper on dynamic retention management is accepted at CONSEQUENCES+REVEAL workshop at #RecSys22. Unfortunately, I cannot attend in-person and had to turn down an oral presentation opportunity, but the poster and the paper will be available at the website.https://t.co/LmJ9PHNmB0
In this paper, the NVIDIA team presents its solution from the @ACMRecSys Challenge for building an effective session-based #recommender using NVIDIA AI for more refined, robust, and accurate recommendations. #RecSys22 #RecSys2022 https://t.co/oBm30bABJi
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