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ORCIDRecSys 2021: Amsterdam, The Netherlands - Challenge
- RecSys Challenge 2021: Proceedings of the Recommender Systems Challenge 2021, Amsterdam, The Netherlands, 1 October 2021. ACM 2021, ISBN 978-1-4503-8693-7
- Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Kristian Lum, Jonathan Hunt, Michael M. Bronstein, Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Wenzhe Shi:
The 2021 RecSys Challenge Dataset: Fairness is not optional. 1-6 - Chris Deotte, Bo Liu, Benedikt Schifferer, Gilberto Titericz:
GPU Accelerated Boosted Trees and Deep Neural Networks for Better Recommender Systems. 7-14 - Michal Daniluk, Jacek Dabrowski, Barbara Rychalska, Konrad Goluchowski:
Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural model. 15-21 - Maksims Volkovs, Felipe Pérez, Zhaoyue Cheng, Jianing Sun, Sajad Norouzi, Anson Wong, Pawel Jankiewicz, Barum Rho:
User Engagement Modeling with Deep Learning and Language Models. 22-27 - Luca Carminati
, Giacomo Lodigiani, Pietro Maldini, Samuele Meta, Stiven Metaj, Arcangelo Pisa, Alessandro Sanvito, Mattia Surricchio, Fernando Benjamín Pérez Maurera, Cesare Bernardis, Maurizio Ferrari Dacrema:
Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained Environment. 28-33 - Pere Gilabert, Santi Seguí:
Addressing the cold-start problem with a two-branch architecture for fair tweet recommendation. 34-38 - Alexander Krauck, David Penz, Markus Schedl:
Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features. 39-43
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