  Modern recommender systems (RS) work by processing a number of signals that
can be inferred from large sets of user-item interaction data. The main signal
to analyze stems from the raw matrix that represents interactions. However, we
can increase the performance of RS by considering other kinds of signals like
the context of interactions, which could be, for example, the time or date of
the interaction, the user location, or sequential data corresponding to the
historical interactions of the user with the system. These complex,
context-based interaction signals are characterized by a rich relational
structure that can be represented by a multi-partite graph. Graph Convolutional
Networks (GCNs) have been used successfully in collaborative filtering with
simple user-item interaction data. In this work, we generalize the use of GCNs
for N-partite graphs by considering N multiple context dimensions and propose a
simple way for their seamless integration in modern deep learning RS
architectures. More specifically, we define a graph convolutional embedding
layer for N-partite graphs that processes user-item-context interactions, and
constructs node embeddings by leveraging their relational structure.
Experiments on several datasets from recommender systems to drug re-purposing
show the benefits of the introduced GCN embedding layer by measuring the
performance of different context-enriched tasks.
