Online Expectation-Maximization for Click Models

Click models allow us to interpret user click behavior in search interactions and to remove various types of bias from user clicks. Existing studies on click models consider a static scenario where user click behavior does not change over time. We show empirically that click models deteriorate over time if retraining is avoided. We then adapt online expectation-maximization (EM) techniques to efficiently incorporate new click/skip observations into a trained click model. Our instantiation of Online EM for click models is orders of magnitude more efficient than retraining the model from scratch using standard EM, while loosing little in quality. To deal with outdated click information, we propose a variant of online EM called EM with Forgetting, which surpasses the performance of complete retraining while being as efficient as Online EM.