Modeling User Navigation on the Web

Understanding how users navigate through the Web is essential for improving user experience. In contrast to traditional approaches, we study contextual and session-based models for user interaction and navigation. We devise generative models for sessions which are augmented by context variables such as timestamps, click metadata, and referrer domains. The probabilistic framework groups similar sessions and naturally leads to a clustering of the data. Alternatively, our approach can be viewed as a behavioral clustering where each user belongs to several clusters. We evaluate our approach on click logs sampled from Yahoo! News. We observe that the incorporation of context leads to interpretable clusterings in contrast to classical approaches. Conditioning the model on the context significantly increases the predictive accuracy for the next click. Our approach consistently outperforms traditional baseline methods and personalized user models.