  We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based
news recommendation that is able to leverage a variety of information types
using Recurrent Neural Networks. We evaluated our approach on two public
datasets, using a temporal evaluation protocol that simulates the dynamics of a
news portal in a realistic way. Our results confirm the benefits of modeling
the sequence of session clicks with RNNs and leveraging side information about
users and articles, resulting in significantly higher recommendation accuracy
and catalog coverage than other session-based algorithms.
