Causality-Based Model for User Profile Construction from Behavior Sequences

This paper presents a novel model for user profile construction using causal relationships. Causal relationships are extracted from behavior sequences to build user profiles. Our model first discovers significant patterns by adapting a new sequence clustering algorithm, and then discovers pattern associations using normalized mutual information (NMI). Causal relationships between significant patterns are then extracted using the transfer entropy approach. These relationships are used to construct causal graphs of activities, to generate the user profile. In extensive experiments on a variety of datasets, we empirically demonstrate that these causality-based profiles yield a significant increase in performance on activity prediction.