Discovering temporal hidden contexts in web sessions for user trail prediction

In many web information systems such as e-shops and information portals, predictive modeling is used to understand user's intentions based on their browsing behaviour. User behavior is inherently sensitive to various hidden contexts. It has been shown in different experimental studies that exploitation of contextual information can help in improving prediction performance significantly. It is reasonable to assume that users may change their intents during one web session and that changes are influenced by some external factors such as switch in temporal context e.g. 'users want to find information about a specific product' and after a while 'they want to buy this product'. A web session can be represented as a sequence of user's actions where actions are ordered by time. The generation of a web session might be influenced by several hidden temporal contexts. Each session can be represented as a concatenation of independent segments, each of which is influenced by one corresponding context. We show how to learn how to apply different predictive models for each segment in this work. We define the problem of discovering temporal hidden contexts in such way that we optimize directly the accuracy of predictive models (e.g. users' trails prediction) during the process of context acquisition. Our empirical study on a real dataset demonstrates the effectiveness of our method.