Content-Based Methods for Predicting Web-Site Demographic Attributes
| Santosh Kabbur, Eui-Hong Han, and George Karypis |
| 10th IEEE International Conference on Data Mining (ICDM), 2010 |
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| 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. |
| Research topics: Data mining | Online advertising | Social networks |