A comparison of click-through rate prediction models in real-time-biddingAlič, Anže (Avtor) Demšar, Jure (Mentor) Hartman, Jan (Komentor) artificial intelligencemachine learningclick-through rate predictionreal-time biddingincremental learningbig datademand-side platformAccurately predicting user clicks is crucial for the success of online advertising in the real-time bidding industry. In this thesis, our goal is to conduct a thorough comparison and evaluation of click-through rate prediction models commonly used in practice. Our advantage lies in our unified implementation approach, which allows for a fair and comprehensive comparison of the models, with the most important contribution being an online A/B test. We evaluated several click-through rate prediction models. Our offline results showed that the DCN-V2 model outperforms the other models in terms of log loss, with DeepFM and DeepFwFM following behind. However, due to domain constraints, the models were subject to underfitting. We demonstrated that in this regard, the DCN-V2 model is again the best as it was the least affected by it. We conducted our evaluation on both a public and a private dataset owned by Outbrain and obtained consistent results. Additionally, we conducted an online A/B test on the production traffic of Outbrain, a demand-side platform in the real-time bidding ecosystem. Our results showed that the DCN-V2 model outperformed the production DeepFM-based model, resulting in a revenue increase of 5.8\%.20232023-06-09 15:05:02Magistrsko delo/naloga146737sl