Download or read book Viewability Prediction for Display Advertising written by Chong Wang and published by . This book was released on 2017 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research is the first to address this important problem of ad viewability prediction. Inspired by the standard definition of viewability, this study proposes to solve the problem from two angles: 1) scrolling behavior and 2) dwell time. In the first phase, ad viewability is predicted by estimating the probability that a user will scroll to the page depth where an ad is located in a specific page view. Two novel probabilistic latent class models (PLC) are proposed. The first PLC model computes constant use and page memberships offline, while the second PLC model computes dynamic memberships in real-time. In the second phase, ad viewability is predicted by estimating the probability that the page depth will be in-view for certain seconds. Machine learning models based on Factorization Machines (FM) and Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) are proposed to predict the viewability of any given page depth in a specific page view. The experiments show that the proposed algorithms significantly outperform the comparison systems.