  Session-based recommendation (SBR) aims at predicting the next item for an
ongoing anonymous session. The major challenge of SBR is how to capture richer
relations in between items and learn ID-based item embeddings to capture such
relations. Recent studies propose to first construct an item graph from
sessions and employ a Graph Neural Network (GNN) to encode item embedding from
the graph. Although such graph-based approaches have achieved performance
improvements, their GNNs are not suitable for ID-based embedding learning for
the SBR task. In this paper, we argue that the objective of such ID-based
embedding learning is to capture a kind of \textit{neighborhood affinity} in
that the embedding of a node is similar to that of its neighbors' in the
embedding space. We propose a new graph neural network, called Graph Spring
Network (GSN), for learning ID-based item embedding on an item graph to
optimize neighborhood affinity in the embedding space. Furthermore, we argue
that even stacking multiple GNN layers may not be enough to encode potential
relations for two item nodes far-apart in a graph. In this paper, we propose a
strategy that first selects some informative item anchors and then encode
items' potential relations to such anchors. In summary, we propose a GSN-IAS
model (Graph Spring Network and Informative Anchor Selection) for the SBR task.
We first construct an item graph to describe items' co-occurrences in all
sessions. We design the GSN for ID-based item embedding learning and propose an
\textit{item entropy} measure to select informative anchors. We then design an
unsupervised learning mechanism to encode items' relations to anchors. We next
employ a shared gated recurrent unit (GRU) network to learn two session
representations and make two next item predictions. Finally, we design an
adaptive decision fusion strategy to fuse two predictions to make the final
recommendation.
