Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Original language	English
Title of host publication	Proceedings of the RecSys Challenge Workshop 2021 at the 15th ACM Conference on Recommender Systems (RecSys 2021)
Number of pages	10
Publication status	Published - 2021

Fields of science

@inproceedings{3315916cce4a418a8232f3c1f79e9dac,

title = "Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features",

author = "A. Krauck and David Penz and Markus Schedl",

year = "2021",

language = "English",

booktitle = "Proceedings of the RecSys Challenge Workshop 2021 at the 15th ACM Conference on Recommender Systems (RecSys 2021)",

}

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features

AU - Krauck, A.

AU - Penz, David

AU - Schedl, Markus

PY - 2021

Y1 - 2021

M3 - Conference proceedings

BT - Proceedings of the RecSys Challenge Workshop 2021 at the 15th ACM Conference on Recommender Systems (RecSys 2021)

ER -