  Solving cold-start problems is indispensable to provide meaningful
recommendation results for new users and items. Under sparsely observed data,
unobserved user-item pairs are also a vital source for distilling latent users'
information needs. Most present works leverage unobserved samples for
extracting negative signals. However, such an optimisation strategy can lead to
biased results toward already popular items by frequently handling new items as
negative instances. In this study, we tackle the cold-start problems for new
users/items by appropriately leveraging unobserved samples. We propose a
knowledge graph (KG)-aware recommender based on graph neural networks, which
augments labelled samples through pseudo-labelling. Our approach aggressively
employs unobserved samples as positive instances and brings new items into the
spotlight. To avoid exhaustive label assignments to all possible pairs of users
and items, we exploit a KG for selecting probably positive items for each user.
We also utilise an improved negative sampling strategy and thereby suppress the
exacerbation of popularity biases. Through experiments, we demonstrate that our
approach achieves improvements over the state-of-the-art KG-aware recommenders
in a variety of scenarios; in particular, our methodology successfully improves
recommendation performance for cold-start users/items.
