Gender Prediction Using Browsing History

Demographic attributes such as gender and age of Internet users provide important information for marketing, personalization, and user behavior research. This paper addresses the problem of predicting users’ gender based on browsing history. We employ a classification-based approach to the problem and investigate a number of features derived from browsing log data. We show that high-level content features such as topics or categories are very predictive of gender and combining such features with features derived from access times and browsing patterns leads to significant improvements in prediction accuracy. We empirically verified the effectiveness of the method on real datasets from Vietnamese online media. The method substantially outperformed a baseline, and achieved a macro-averaged F1 score of 0.805. Experimental results also demonstrate the effectiveness of combining different feature types: a combination of features achieved 12% improvement of F1 score over the best performing individual feature type.