Feature extraction from web browsing history of one website
What you describe uses the data that the paths can offer. You can easily generate features from the data and time. For instance, given the date, you can generate a categorical variable denoting the weekday (Monday, Tuesday, etc..). Given the timestamp, you can generate binary variables to partition the day in four or more partitions: is_morning, is_afternoon etc.. Somebody may only read in the morning or at night, and the aim of these features is to capture this. 

Further, you can get the interactions between weekdays and day partitions. Such features may help to distinguish users that in the Sunday mornings read about sports while Monday mornings they are at work and read financial news. Be careful of the overfitting though. Note that trees have been shown to capture such complex interactions; given them explicitly is beneficial though.