Probabilistic Deduplication of Anonymous Web Traffic

Cookies and log in-based authentication often provide incomplete data for stitching website visitors across multiple sources, necessitating probabilistic deduplication. We address this challenge by formulating the problem as a binary classification task for pairs of anonymous visitors. We compute visitor proximity vectors by converting categorical variables like IP addresses, product search keywords and URLs with very high cardinalities to continuous numeric variables using the Jaccard coefficient for each attribute. Our method achieves about 90% AUC and F-scores in identifying whether two cookies map to the same visitor, while providing insights on the relative importance of available features in Web analytics towards the deduplication process.