Abstract is missing.
- The 2021 RecSys Challenge Dataset: Fairness is not optionalLuca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain 0001, Yuanpu Xie, Kristian Lum, Jonathan Hunt, Michael M. Bronstein, Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Wenzhe Shi. 1-6 [doi]
- GPU Accelerated Boosted Trees and Deep Neural Networks for Better Recommender SystemsChris Deotte, Bo Liu, Benedikt Schifferer, Gilberto Titericz. 7-14 [doi]
- Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural modelMichal Daniluk, Jacek Dabrowski 0004, Barbara Rychalska, Konrad Goluchowski. 15-21 [doi]
- User Engagement Modeling with Deep Learning and Language ModelsMaksims Volkovs, Felipe Pérez, Zhaoyue Cheng, Jianing Sun, Sajad Norouzi, Anson Wong, Pawel Jankiewicz, Barum Rho. 22-27 [doi]
- Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained EnvironmentLuca Carminati, Giacomo Lodigiani, Pietro Maldini, Samuele Meta, Stiven Metaj, Arcangelo Pisa, Alessandro Sanvito, Mattia Surricchio, Fernando Benjamín Pérez Maurera, Cesare Bernardis, Maurizio Ferrari Dacrema. 28-33 [doi]
- Addressing the cold-start problem with a two-branch architecture for fair tweet recommendationPere Gilabert, Santi Seguí. 34-38 [doi]
- Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User FeaturesAlexander Krauck, David Penz, Markus Schedl. 39-43 [doi]