Optimizing Click-Through Rate in Online Advertising Using Cost-Sensitive LearningPetek, Bernarda (Avtor) Bosnić, Zoran (Mentor) Jakomin, Martin (Komentor) real-time biddingclick-through rate predictioncost-sensitive learningcustom loss functionReal-time bidding is a type of online advertising, which displays personalized advertisements online to users based on their interests in real time. Demand-side platforms participate in such bidding for ad spaces. The bidding price, usually computed using a predicted click-through rate and target cost-per-click, reflects the value of an advertisement to the bidder. The primary focus of research in online advertising revolves around improving the prediction of click-through-rate. We focus on improving click-through-rate prediction for higher-cost advertisements, as they are typically less represented in the dataset, while keeping performance of lower-cost advertisements unaffected. Our approach involves the implementation of cost-sensitive machine learning by weighting the loss function. We explore various mappings of target cost-per-click values as weights. We train our model on the real-world like dataset, using weighted loss functions, resulting in different machine learning models. We evaluate the results with log-loss and calibration metrics. Our results reveal promising outcomes, indicating that some weights improve click-through-rate prediction for higher-cost advertisements while maintaining the quality for lower-cost advertisements.20232023-09-13 08:15:57Magistrsko delo/naloga150023sl