RecSys Challenge 2014
Róbert Pálovics, Frederick Ayala-Gómez, Balázs Csikota, Bálint Daróczy, Levente Kocsis, Dominic Spadacene, András A. Benczúr · 2014
In this paper we give our solution to the RecSys Challenge 2014. In our ensemble we use (1) a mix of binary classification methods for predicting nonzero engagement, including logistic regression and SVM; (2) regression methods for directly predicting the engagement, including linear regression and gradient boosted trees; (3) matrix factorization and factorization machines over the user-movie matrix, by using user and movie features as side information. For most of the methods, we use the GraphLab Create implementation. Our current [email protected] achieves 0.874. We release our experiments as IPython Notebooks.