RecSys Challenge 2014: an ensemble of binary classifiers and matrix factorization

    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 nDCG@10 achieves 0.874. We release our ex-
    periments as IPython Notebooks.

    Pálovics, R., Ayala-Gómez, F., Csikota, B., Daróczy, B., Kocsis, L., Spadacene, D., Benczúr, A. A.
    Proceedings of the 2014 Recommender Systems Challenge