Abstract recognizing Web browsing signatures can complement other behavioral biometrics such as keystroke authentication to verify a claim of identity and/or identify persons of interest. The deluge of available digital traces enables the cognitive analysis of behavioral traits that differentiate between users and predict their online behavior. Recommendation systems have long capitalized on this capability to personalize search queries but have not exploited the temporal structure of preferences. This paper claims that spatio-temporal patterns of category of website visited by time of access can uniquely characterize and identify users. We present some exploratory approaches in user identification based on recurrent neural networks and empirical results based on clickstream data obtained through a user study and through an internet data provider.