Parallel News-Article Traffic Forecasting with ADMM

author: Stratis Ioannidis, Department of Electrical and Computer Engineering, Northeastern University
published: Oct. 12, 2016,   recorded: August 2016,   views: 1108
Categories

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

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 page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: