Feature embedding in click-through rate predictionPahor, Samo (Avtor) Demšar, Jure (Mentor) real-time biddingclick-through rate predictionfeature embeddingfeature transformationOnline advertising allows companies to reach a worldwide user base and engage key demographics to market their products. The cornerstone of modern online advertising is participating in real-time bidding (RTB) auctions, where ad space on websites is being dynamically sold to the highest bidder. A key part of RTB auctions is predicting click-through rate (CTR), or the probability that a user will click on a displayed ad. CTR prediction is performed with machine learning, where click responses are modeled based on a diverse set of contextual and historical features. We tackle the challenge of feature embedding for the purposes of improving the CTR prediction process. We focus on two popular prediction models, logistic regression and factorization machines, and propose different feature embedding modules to improve CTR prediction. We test the predictive performance of our augmented models on an offline dataset, which resembles production data, provided by Zemanta. Our results show that several proposed embedding modules provide an important increase in predictive performance without a drastic increase in training time or model size.20212021-11-18 15:10:00Magistrsko delo/naloga133259VisID: 28961COBISS_ID: 87422467sl