Multi-task learning in programmatic advertisingVreš, Domen (Avtor) Robnik Šikonja, Marko (Mentor) Jakomin, Martin (Komentor) artificial intelligencemachine learningmulti-task learningreal-time biddingviewability predictionclick-through rate predictionconversion rate predictiondeep & cross networkcross-stitch layersoft attentionuncertainty based loss weighingWith the popularity of the world wide web, online advertising became crucial for the advertising industry. A large part of online advertising is based on real-time bidding, where automated software is used to buy and sell ad space. In this ecosystem, demand-side platforms (DSP) work with advertisers to display their ads on websites by bidding for ad space. Three crucial problems for DSP companies are viewability prediction, click-through rate (CTR) prediction, and conversion rate (CVR) prediction. In this work, we model these three problems using multi-task learning, which was not done before. We take the deep & cross network architecture, a state-of-the-art for CTR and CVR prediction, and expand it into a multi-task model. Additionally, we expand the multi-task model with recent techniques of cross-stitch layer, soft attention mask, uncertainty-based loss weighing, and relationship layer. We compare the multi-task model with single-task baselines on a large proprietary real-world data set. We show that the multi-task models significantly outperform the CVR baseline, which is our main goal, as CVR prediction is the most difficult task to model.20232023-09-28 11:25:00Magistrsko delo/naloga151061VisID: 35431COBISS_ID: 170173187sl