Overcoming browser cookie churn with clustering

Abstract

Many large Internet websites are accessed by users anonymously, without requiring registration or logging-in. However, to provide personalized service these sites build anonymous, yet persistent, user models based on repeated user visits. Cookies, issued when a web browser first visits a site, are typically employed to anonymously associate a website visit with a distinct user (web browser). However, users may reset cookies, making such association short-lived and noisy. In this paper we propose a solution to the cookie churn problem: a novel algorithm for grouping similar cookies into clusters that are more persistent than individual cookies. Such clustering could potentially allow more robust estimation of the number of unique visitors of the site over a certain long time period, and also better user modeling which is key to plenty of web applications such as advertising and recommender systems.