Abstract: 

Session-based recommendation (SBR) aims to predict the next item based on a set of anonymous sessions. Capturing user intent from a short interaction sequence imposes different technical challenges since no user profiles are available and interaction data is naturally sparse. Recent approaches rely on graph neural networks (GNNs) to model sessions and global item relations. In this talk we present two different approaches incorporating graph-knowledge into the session-based recommendation scenario. First, we create a feature-rich item co-occurrence graph and learn the corresponding item embeddings in an unsupervised manner. These embeddings deal as input to several SOTA sequential models to tackle SBR. As a second approach, we propose to explicitly model the information aggregation mechanism over multiple layers of GNNs via shortest-path edges based on knowledge from the sequential recommendation domain. This approach does not require multiple layers to exchange information and implicitly filters unreliable item-item relations. Furthermore, to address inherent data sparsity, we are the first to apply supervised contrastive learning at the session level by mining data-driven hard negative samples.