Graph Convolution Machine for Context-aware Recommender System

The latest advance in recommendation shows that
better user and item representations can be learned
via performing graph convolutions on the user-item
interaction graph. However, such finding is mostly
restricted to the collaborative filtering (CF) sce-
nario, where the user/item attributes and interaction
contexts are not available. In this work, we extend
the advantages of graph convolutions to context-
aware recommender system (CARS), which repre-
sents a generic type of models that can handle var-
ious side information. We propose Graph Convo-
lution Machine (GCM), an end-to-end framework
that consists of three components: an encoder,
graph convolution (GC) layers, and a decoder. The
encoder projects users, items, and contexts into em-
bedding vectors, which are passed to the GC layers
that refine user and item embeddings with context-
aware graph convolutions on user-item graph. The
decoder digests the refined embeddings to output
the prediction score by considering the interactions
among user, item, and context embeddings. We
conduct experiments on three real-world datasets
from Yelp, validating the effectiveness of GCM and
the benefits of performing graph convolutions for
CARS.

• Machine intelligence