Top Tweets for #RecSysChallenge
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【解法紹介】RecSys Challenge 2025 で優勝しました
https://t.co/SUd9RHGV3e
RecSys Challenge 2025で取り組んだ課題と得られた学びについて紹介しています💡
ぜひご覧ください！
#RecSysChallenge
【ブログ記事公開のお知らせ】
RecSys Challenge 2025 で優勝しました
https://t.co/ekiCiaKGfp
「どのようにして優勝を掴んだのか」、その技術と挑戦をまとめたレポート記事を公開！
ぜひご覧ください💡
#RecSysChallenge #データサイエンス #推薦システム
The #RecSysChallenge workshop has officially begun! Join us in the room “Aula Magna” for an exciting keynote by @Lindskow. Don't miss it!
@ACMRecSys #recsys2024
Ekstra, Ekstra!
We are excited to announce the 2024 #RecSysChallenge!
This year, the data will be provided by the Danish news website Ekstra Bladet. Check out all the details on the Challenge website:
https://t.co/zVkJLEc5B2
Submissions open on March 25. Deadline: June 14.
All set for @ACMRecSys #recsyschallenge on day two of #recsys2023. Do join us if you are around or attending via #recsyshub #recsys . Thanks again to @sharechatapp for sponsoring it this year.
Not the #RecSysChallenge, but a personalized fashion #recsys challenge from @hm on Kaggle https://t.co/9sP0r48oJ2
Pere Gilabert @perecasxiru is now presenting at #RecSys2021 his solution on #Twitter recomendation. 2n place in the Academic leaderboard from #recsyschallenge!! Congratulations!! @recsyschallenge #datascienceUB #recsys
RecSys Challenge 2021 with a Fairness Focus. Join this chat if you are interested! #recsyschallenge #fairness #twitter
Tomorrow! Learn about how we've introduced a fairness metric to the recsys challenge this year. It's about time we incentivized the community to think of more than model 'accuracy.'
#LessonsLearned from hosting the #Twitter ACM #RecSysChallenge 2020, shared by Michael Bronstein et al.
https://t.co/J9dwLSZbHw #RS_c #RecSys
The Million Playlist Dataset (MPD) has finally been released. This is the one we used for #RecSysChallenge in 2018. Special thanks to @cweichen and @SpotifyResearch for making this happen.
#RecSys2020
NVIDIA https://t.co/adW16n3XBA team, the winners of the #RecSysChallenge2020 at @ACMRecSys, donated their prize (15000$) to Black in AI organization 👏👏👏👏
I am glad to announce that our team of master degree students from @polimi ranked 4 in the #RecSysChallenge 2020 with team BanaNeverAlone!
We have released the source code and a preprint of the article decribing our approach.
https://t.co/iyTqJWEB66
#recsys #twitter
The "Session-based hotel recommendation dataset" by Trivago has been released. This a rich and large-scale dataset with various types of user interactions: https://t.co/P4kdjY6bNL (link inside the paper)
@trivago @JensAdamczak @peter_knees #sessionbased, #hotel #recsyschallenge
Recsys 2020 challenge is now concluded! We had 1000+ teams registered and top 3 teams are Nvidia, learner and Wantedly. This is probably first time in a few years that the top performance teams are not even close! Check out the results under https://t.co/hdK3KCueCa.
It’s a wrap! Thanks to everyone who helped us making this possible, from our colleagues @Twitter to the @recsyschallenge participants! #RecSys #RecSysChallenge
Recsys 2020 challenge is now concluded! We had 1000+ teams registered and top 3 teams are Nvidia, learner and Wantedly. This is probably first time in a few years that the top performance teams are not even close! Check out the results under https://t.co/hdK3KCueCa.
I have noticed the Spotify dataset page lists the Million Playlists Dataset used in the RecSys challenge 2019 but the link to the data is not working @SpotifyResearch
https://t.co/7pA6tNWlDH
#recsyschallenge
New dataset coming soon for recsys and #NLProc
Twitter will release a large public dataset of ~200M public tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and tweet features. https://t.co/i3ufI87ZJx
A-HA: A Hybrid Approach for Hotel Recommendation
Won the 4th place (4/1564) of the 2019 ACM RecSys Challenge.
Check out our source code here:
https://t.co/NA4c0ZRiuB
@steeve__huang #recsys2019 #recsyschallenge
Great to see that our paper contribution to the @recsyschallenge has been accepted. Our students did a terrific job!
"Leveraging laziness, Browsing-Pattern Aware Stacked Models for Sequential Accommodation Learning to Rank" #recsys19 #recsyschallenge https://t.co/xZOitNgZ4A
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