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RecSys 2020: Virtual Event, Brazil - Challenge
- RecSys Challenge '20: Proceedings of the Recommender Systems Challenge 2020, Virtual Event Brazil, September, 2020. ACM 2020, ISBN 978-1-4503-8835-1
- Sumit Sidana:
A combination of classification based methods for recommending tweets. 1-5 - Shuhei Goda, Naomichi Agata, Yuya Matsumura:
A Stacking Ensemble Model for Prediction of Multi-type Tweet Engagements. 6-10 - Carlos Miguel Patiño, Camilo Velásquez, Juan Manuel Muñoz, Juan Manuel Gutiérrez, David Ricardo Valencia, Cristian Bartolome Aramburu:
Leveraging User Embeddings and Text to Improve CTR Predictions With Deep Recommender Systems. 11-15 - Benedikt Schifferer, Gilberto Titericz, Chris Deotte, Christof Henkel, Kazuki Onodera, Jiwei Liu, Bojan Tunguz, Even Oldridge, Gabriel de Souza Pereira Moreira, Ahmet Erdem:
GPU Accelerated Feature Engineering and Training for Recommender Systems. 16-23 - Pere Gilabert
, Santi Seguí
:
Gradient Boosting and Language Model Ensemble for Tweet Recommendation. 24-28 - Nicolò Felicioni
, Andrea Donati, Luca Conterio, Luca Bartoccioni, Davide Yi Xian Hu
, Cesare Bernardis, Maurizio Ferrari Dacrema
:
Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction. 29-33 - Seyed Ali Alhosseini, Raad Bin Tareaf
, Christoph Meinel:
Engaging with Tweets: The Missing Dataset On Social Media. 34-37 - Maksims Volkovs, Zhaoyue Cheng, Mathieu Ravaut, Hojin Yang, Kevin Shen, Jin Peng Zhou, Anson Wong, Saba Zuberi, Ivan Zhang, Nick Frosst, Helen Ngo, Carol Chen, Bharat Venkitesh, Stephen Gou, Aidan N. Gomez:
Predicting Twitter Engagement With Deep Language Models. 38-43 - Dietmar Jannach, Gabriel de Souza Pereira Moreira, Even Oldridge:
Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?: A Position Paper. 44-49
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