  Recently, Recurrent Neural Networks (RNNs) have been applied to the task of
session-based recommendation. These approaches use RNNs to predict the next
item in a user session based on the previ- ously visited items. While some
approaches consider additional item properties, we argue that item dwell time
can be used as an implicit measure of user interest to improve session-based
item recommen- dations. We propose an extension to existing RNN approaches that
captures user dwell time in addition to the visited items and show that
recommendation performance can be improved. Additionally, we investigate the
usefulness of a single validation split for model selection in the case of
minor improvements and find that in our case the best model is not selected and
a fold-like study with different validation sets is necessary to ensure the
selection of the best model.
