  The goal of session-based recommendation (SR) models is to utilize the
information from past actions (e.g. item/product clicks) in a session to
recommend items that a user is likely to click next. Recently it has been shown
that the sequence of item interactions in a session can be modeled as
graph-structured data to better account for complex item transitions. Graph
neural networks (GNNs) can learn useful representations for such
session-graphs, and have been shown to improve over sequential models such as
recurrent neural networks [14]. However, we note that these GNN-based
recommendation models suffer from popularity bias: the models are biased
towards recommending popular items, and fail to recommend relevant long-tail
items (less popular or less frequent items). Therefore, these models perform
poorly for the less popular new items arriving daily in a practical online
setting. We demonstrate that this issue is, in part, related to the magnitude
or norm of the learned item and session-graph representations (embedding
vectors). We propose a training procedure that mitigates this issue by using
normalized representations. The models using normalized item and session-graph
representations perform significantly better: i. for the less popular long-tail
items in the offline setting, and ii. for the less popular newly introduced
items in the online setting. Furthermore, our approach significantly improves
upon existing state-of-the-art on three benchmark datasets.
