RecSys Challenge 2015: ensemble learning with categorical features
By Peter Romov, Evgeny Sokolov. 2015
45 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Flavian Vasile
- Elena Smirnova
- Alexis Conneau
- Graham Neubig
- Furu Wei
- Sebastian Ruder
- Luke S. Zettlemoyer
- Mohit Bansal
In Computer Vision and Pattern Recognition
- Alexis Conneau
- Flavian Vasile
- Elena Smirnova
- Mohit Bansal
- Furu Wei
- Graham Neubig
- Luke S. Zettlemoyer
- Li Dong
Citations in the corpus
- Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks (2017, background): “In particular, 2015 RecSys Challenge winners [16] augmented a gradient boosting algorithm with time features and counters.”
- A Time-Aware Exploration of RecSys15 Challenge Dataset (2016, unknown): “[8], which scored ∼ 63,000.”
- Machine learning and AI in marketing – Connecting computing power to human insights (2020, unknown): “Feature engineering is frequently cited as a key success factor in winning entries of data science contests (e.g. Romov & Sokolov, 2015).”
- Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation (2016, methodology): “In the context of product recommendation, they were successfully applied to ad recommendations in Yahoo! Mail [9], for Restaurant recommendations by OpenTable [1] and in the 1st prize winners in the 2015 RecSys Challenge [26].”