Abstract is missing.
- A combination of classification based methods for recommending tweetsSumit Sidana. 1-5 [doi]
- A Stacking Ensemble Model for Prediction of Multi-type Tweet EngagementsShuhei Goda, Naomichi Agata, Yuya Matsumura. 6-10 [doi]
- Leveraging User Embeddings and Text to Improve CTR Predictions With Deep Recommender SystemsCarlos Miguel Patiño, Camilo Velásquez, Juan Manuel Muñoz, Juan Manuel Gutiérrez, David Ricardo Valencia, Cristian Bartolome Aramburu. 11-15 [doi]
- GPU Accelerated Feature Engineering and Training for Recommender SystemsBenedikt Schifferer, Gilberto Titericz, Chris Deotte, Christof Henkel, Kazuki Onodera, Jiwei Liu, Bojan Tunguz, Even Oldridge, Gabriel de Souza Pereira Moreira, Ahmet Erdem. 16-23 [doi]
- Gradient Boosting and Language Model Ensemble for Tweet RecommendationPere Gilabert, Santi Seguí. 24-28 [doi]
- Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement PredictionNicolò Felicioni, Andrea Donati, Luca Conterio, Luca Bartoccioni, Davide Yi Xian Hu, Cesare Bernardis, Maurizio Ferrari Dacrema. 29-33 [doi]
- Engaging with Tweets: The Missing Dataset On Social MediaSeyed Ali Alhosseini, Raad Bin Tareaf, Christoph Meinel. 34-37 [doi]
- Predicting Twitter Engagement With Deep Language ModelsMaksims 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. 38-43 [doi]
- Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?: A Position PaperDietmar Jannach, Gabriel de Souza Pereira Moreira, Even Oldridge. 44-49 [doi]