Evaluating Model Tradeoffs for Click PredictionKalc, Matej (Avtor) Demšar, Jure (Mentor) Kopič, Davorin (Komentor) Hartman, Jan (Komentor) CTR predictionFactorization machinesDeep learningOnline learningBayesian optimizationIn the context of online advertising, Click-Through Rate (CTR) is the probability that a user clicks on an ad. CTR prediction is done using machine learning methods, such as Factorization machines (FM) and neural networks. Various improved versions of these traditional approaches have been proposed in the last decade, the main goal of this thesis is to evaluate these upgrades. We evaluated the models in two phases: using different combinations of parameters and using Bayesian optimization for parameter tuning. In the first phase, results showed that the group of models that use neural networks achieves a higher Area Under the ROC Curve (AUC). Kernel-extended Factorization Machine, a new proposed model during the Data Science project competition at the Faculty of Computer Science, performed worse than the FM model. In the second phase, we applied Bayesian optimization to the models to achieve an even higher AUC. The second-generation of the Deep&Cross model surprisingly surpassed Deep Factorization Machine with a higher AUC, which had the highest AUC in the first phase. During the evaluation, we also tested the degree of FM and concluded that there is no need for a degree higher than two.20212021-09-08 16:05:01Diplomsko delo/naloga129844sl