The ever-growing and dynamic nature of user-generated data in online systems poses obvious challenges on how we learn from such data. The
underlying problem is how to adapt, in real time, to multiple simultaneous changes involving individual users, user contexts and the system
as a whole. Many algorithms are able to adjust their output to some of these changes in real time, however, this requires that the model has
been previously trained on data with very similar phenomena. To adapt to new trends, preferences and other unpredictable phenomena, algorithms
must be able to update the underlying model itself, which should preferably happen online, incrementally and in real time. This motivates the
research on adaptive methods able to maintain and evolve predictive models over time. Incremental learning algorithms and data stream mining
have gained maturity in recent years, however this body of knowledge has not transitioned to predictive user modeling, and although the
potential to solve relevant problems is high, advances in this direction are far from trivial. This calls for further investigation and
scientific contributions.
