  Given the convenience of collecting information through online services,
recommender systems now consume large scale data and play a more important role
in improving user experience. With the recent emergence of Graph Neural
Networks (GNNs), GNN-based recommender models have shown the advantage of
modeling the recommender system as a user-item bipartite graph to learn
representations of users and items. However, such models are expensive to train
and difficult to perform frequent updates to provide the most up-to-date
recommendations. In this work, we propose to update GNN-based recommender
models incrementally so that the computation time can be greatly reduced and
models can be updated more frequently. We develop a Graph Structure Aware
Incremental Learning framework, GraphSAIL, to address the commonly experienced
catastrophic forgetting problem that occurs when training a model in an
incremental fashion. Our approach preserves a user's long-term preference (or
an item's long-term property) during incremental model updating. GraphSAIL
implements a graph structure preservation strategy which explicitly preserves
each node's local structure, global structure, and self-information,
respectively. We argue that our incremental training framework is the first
attempt tailored for GNN based recommender systems and demonstrate its
improvement compared to other incremental learning techniques on two public
datasets. We further verify the effectiveness of our framework on a large-scale
industrial dataset.
