Parallel News-Article Traffic Forecasting with ADMM
published: Oct. 12, 2016, recorded: August 2016, views: 1097
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Predicting the traffic of an article, as measured by page views, is of great importance to content providers. Articles with increased traffic can improve advertising revenue and expand a provider’s user base. We propose a broadly applicable methodology incorporating meta-data and joint forecasting across articles, that involves solving a large optimization problem through the Alternating Directions Method of Multipliers (ADMM). We implement our solution using Spark, and evaluate it over a large corpus of articles and forecasting models. Our results demonstrate that our featurebased forecasting is both scalable as well as highly accurate, significantly improving forecasting predictions compared to traditional forecasting models.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !