Swing or not to Swing: Learn When (not) to Advertise

by Andrei Broder, Massimiliano Ciaramita et al.

This paper talks about how to learn to show the ads or not in some cases. The basic idea is to learn a classification model based on candidate sets's features, like relevance feature, cohesiveness features. This is compared to a thresholding method, which can be trivially obtained via commonly used retrieval system of ads and proved more effective when our goal is not to maintain a high recall rate (therefore the more the better case).

Another idea is to train the model based on click feedbacks instead of editorial judgement. The paper mentioned to use online learning techniques, which is the reason that most online serving models has to take into account of the time-evolving property and allows the model to be trained in an incremental style.