Content-Based Methods for Predicting Web-Site Demographic Attributes

Loading...
Thumbnail Image

View/Download File

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Content-Based Methods for Predicting Web-Site Demographic Attributes

Published Date

2010-09-17

Publisher

Type

Report

Abstract

Demographic information plays an important role in gaining valuable insights about a web-site's user-base and is used extensively to target online advertisements and promotions. This paper investigates machine-learning approaches for predicting the demographic attributes of web-sites using information derived from their content and their hyperlinked structure and not relying on any information directly or indirectly obtained from the web-site's users. Such methods are important because users are becoming increasingly more concerned about sharing their personal and behavioral information on the Internet. Regression-based approaches are developed and studied for predicting demographic attributes that utilize different content-derived features, different ways of building the prediction models, and different ways of aggregating web-page level predictions that take into account the web's hyperlinked structure. In addition, a matrix-approximation based approach is developed for coupling the predictions of individual regression models into a model designed to predict the probability mass function of the attribute. Extensive experiments show that these methods are able to achieve an RMSE of 8--10% and provide insights on how to best train and apply such models.

Keywords

Description

Related to

Replaces

License

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Kabbur, Santosh; Han, Euihong; Karypis, George. (2010). Content-Based Methods for Predicting Web-Site Demographic Attributes. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215839.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.