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ORCIDRecSys 2016: Boston, MA, USA - Challenge
- Fabian Abel, András A. Benczúr, Daniel Kohlsdorf, Martha A. Larson, Róbert Pálovics:
Proceedings of the 2016 Recommender Systems Challenge, RecSys Challenge 2016, Boston, Massachusetts, USA, September 15, 2016. ACM 2016, ISBN 978-1-4503-4801-0 - Mirko Polato
, Fabio Aiolli:
A preliminary study on a recommender system for the job recommendation challenge. 1:1-1:4 - Chenrui Zhang, Xueqi Cheng:
An ensemble method for job recommender systems. 2:1-2:4 - Jose Ignacio Honrado, Oscar Huarte, Cesar Jimenez, Sebastian Ortega, José R. Pérez-Agüera, Joaquín Pérez-Iglesias, Álvaro Polo, Gabriel Rodríguez:
Jobandtalent at RecSys Challenge 2016. 3:1-3:5 - Sonu K. Mishra, Manoj Reddy:
A bottom-up approach to job recommendation system. 4:1-4:4 - Toon De Pessemier, Kris Vanhecke, Luc Martens:
A scalable, high-performance Algorithm for hybrid job recommendations. 5:1-5:4 - Vasily A. Leksin, Andrey Ostapets:
Job recommendation based on factorization machine and topic modelling. 6:1-6:4 - Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, Prem Natarajan:
Temporal learning and sequence modeling for a job recommender system. 7:1-7:4 - Tommaso Carpi, Marco Edemanti, Ervin Kamberoski, Elena Sacchi, Paolo Cremonesi, Roberto Pagano, Massimo Quadrana:
Multi-stack ensemble for job recommendation. 8:1-8:4 - Dávid Zibriczky:
A combination of simple models by forward predictor selection for job recommendation. 9:1-9:4 - Andrzej Pacuk, Piotr Sankowski, Karol Wegrzycki, Adam Witkowski, Piotr Wygocki:
RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting. 10:1-10:4 - Wenming Xiao, Xiao Xu, Kang Liang, Junkang Mao, Jun Wang:
Job recommendation with Hawkes process: an effective solution for RecSys Challenge 2016. 11:1-11:4
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