Online Limited-Memory BFGS for Click-Through Rate Prediction

We study the problem of click-through rate (CTR) prediction, where the goal is to predict the probability that a user will click on a search advertisement given information about his issued query and account. In this paper, we formulate a model for CTR prediction using logistic regression, then assess the performance of stochastic gradient descent (SGD) and online limited-memory BFGS (oLBFGS) for use in training the corresponding classifier. We demonstrate empirically that oLBFGS provides faster convergence and requires fewer training examples than SGD to achieve comparable performance, confirming the benefits of the use of second-order information in stochastic optimization.