User behaviour in the web site can be modelled from two basic points of view. The first one is the short term behaviour, which reflect user’s actual intent, preferences, goal etc. It captures user’s most actual behaviour and actions but it is typically very noisy, because of influence of user’s actual context, mood and more unpredictable conditions.
The second point of view – long-term behaviour is characterized by more stable preferences identification and capturing user typical customs. On the other side, this kind of behaviour is not so adaptable to changes, it learn trends and hot topics of user behaviour only after longer time period.
To be able to model user preferences and predict future behaviour, it is suitable to combine both data sources and consider them when estimating next user actions. In our research, we focus on task of user session exit intent prediction. This task require to be able to recognize subtle changes in user behaviour in comparison to previous behaviour in different time periods as well as characteristics of actual user session.