  News recommender systems are designed to surface relevant information for
online readers by personalizing their user experiences. A particular problem in
that context is that online readers are often anonymous, which means that this
personalization can only be based on the last few recorded interactions with
the user, a setting named session-based recommendation. Another particularity
of the news domain is that constantly fresh articles are published, which
should be immediately considered for recommendation. To deal with this item
cold-start problem, it is important to consider the actual content of items
when recommending. Hybrid approaches are therefore often considered as the
method of choice in such settings. In this work, we analyze the importance of
considering content information in a hybrid neural news recommender system. We
contrast content-aware and content-agnostic techniques and also explore the
effects of using different content encodings. Experiments on two public
datasets confirm the importance of adopting a hybrid approach. Furthermore, we
show that the choice of the content encoding can have an impact on the
resulting performance.
