Using market exploration to deal with censored data in real-time biddingHartman, Jan (Avtor) Demšar, Jure (Mentor) Štrumbelj, Erik (Komentor) censored dataclick-through rate predictionreal-time biddingincremental learningbig datademand-side platformReal-time bidding is a fast-growing part of online advertising in which ad space on websites is sold in real-time while the page is still loading. The ad space is sold in auctions where several bidders compete. One of the central problems in RTB is click-through rate prediction, which has to deal with censored data -- since the bidders do not receive data about the auctions they lose, the predictive models cannot learn from them. To tackle this problem, we propose two strategies that explore by buying more ad impressions on unknown parts of the market. The proposed strategies use either hand-crafted insights or model uncertainty to guide the exploration. To test the strategies in the real world, we conducted A/B tests on the production traffic of Zemanta, a DSP in the RTB ecosystem. We also compared the obtained models' performances offline. Our results show that exploring the market through publishers did not bring significant improvements to the business or the model metrics. On the other hand, exploring with the uncertainty of the predictions showed increases in revenue and CTR as well as improvements in model performance metrics, indicating that using the uncertainty of the CTR model for exploration can be beneficial.20212021-09-30 10:10:00Magistrsko delo/naloga131620sl