Combined Regression and Tripletwise Learning for Conversion Rate Prediction in Real-Time Bidding Advertising

Lili Shan, Lei Lin, Chengjie Sun

Abstract: In real-time bidding advertising (RTB), the buyers bid for individual advertisement impressions provided by publishers in real time. The final goal of the buyers is to maximize the return on their investment. To gain higher returns, buyers prefer to first purchase more conversion impressions than click-only ones and then purchase more click-only impressions prior to non-click ones. Simultaneously, to reduce the expense, they need to accurately estimate a reasonable bid price, the predicted precision of which depends on the precision of the predicted conversion rate (CVR) or predicted click-through rate (CTR). Therefore, the predicted CVR or predicted CTR must provide not only good ranking values but also correct regression estimations. This paper is focused on the CVR estimation problem for buy-sides in RTB and a combined regression and tripletwise ranking method (CRT) is proposed that jointly considers regression loss and tripletwise ranking loss to estimate the CVR. This method attempts to rank conversion impressions above click-only ones and simultaneously rank click-only impressions above non-click ones. Meanwhile, through simultaneously utilizing the historical conversion and click information to alleviate sparsity, the CRT method is also aimed to achieve a good two category-ranking performance, as well as a good regression performance for predicting the CVR.