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1 Among the many significant advances in the area of deep learning, the Natural Language Processing (NLP) space holds a special place. The availability of very large datasets along with the existence of powerful computing environments have created a fascinating environment for researchers. One of the algorithms recently developed is Word2Vec, which enables the creation of embeddings (lowdimensional, meaningful representations of language that can be used for machine learning tasks such as prediction or classification). In this study, we experiment with Word2Vec and apply it to a different domain, i.e., representation of user behavior in information systems. We demonstrate how feature engineering tasks for user behavior characterization can be enriched by the use of NLP concepts.

Can language help in the characterization of user behavior? Feature engineering experiments with Word