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Probabilistic Relational Models of On-line User Behavior Early Explorations

We propose the usefulness of probabilistic relational methods for modeling user behavior at web sites. Web logs (aka "click streams"), server logs, and other data sources, taken as datasets for traditional machine learning algorithms, violate the iid assumption of most algorithms. Requests ("clicks") are not independent within a session, sessions for a visitor are not independent of one another, and page types, in their interaction with behavioral profile, are highly correlated, both by static link structure and dynamic navigation sequence. We introduce probabilistic relational modeling, and compare a series of increasingly sophisticated models, ranging from a simple, non-relational Bayesian network model of a click through traditional Hidden Markov Models (HMMs) to a new representation, a fully relational extensions of HMMs, that includes visitors, sessions, clicks, and pages as participating entities. We measure the performance of the series of models on the task of predicting whether or not the current request is the last in a session. Results show a significant increase in performance, as measured by ROC AUC (area under the curve).