Sequence learning using content and consumption patterns for user path prediction

We introduce a novel model for future/next page prediction in online user journeys that uses a combination of doc2vec webpage representations with an LSTM-based neural network to mine patterns from users' online navigational paths combined with their content preferences. Empirical explorations show promise towards creating customized user experiences leveraging this work.