  Collaborative filtering has been largely used to advance modern recommender
systems to predict user preference. A key component in collaborative filtering
is representation learning, which aims to project users and items into a low
dimensional space to capture collaborative signals. However, the scene
information, which has effectively guided many recommendation tasks, is rarely
considered in existing collaborative filtering methods. To bridge this gap, we
focus on scene-based collaborative recommendation and propose a novel
representation model SceneRec. SceneRec formally defines a scene as a set of
pre-defined item categories that occur simultaneously in real-life situations
and creatively designs an item-category-scene hierarchical structure to build a
scene-based graph. In the scene-based graph, we adopt graph neural networks to
learn scene-specific representation on each item node, which is further
aggregated with latent representation learned from collaborative interactions
to make recommendations. We perform extensive experiments on real-world
E-commerce datasets and the results demonstrate the effectiveness of the
proposed method.
