  In a practical recommender system, new interactions are continuously
observed. Some interactions are expected, because they largely follow users'
long-term preferences. Some other interactions are indications of recent trends
in user preference changes or marketing positions of new items. Accordingly,
the recommender needs to be periodically retrained or updated to capture the
new trends, and yet not to forget the long-term preferences. In this paper, we
propose a novel and generic retraining framework called Disentangled
Incremental Learning (DIL) for graph-based recommenders. We assume that
long-term preferences are well captured in the existing model, in the form of
model parameters learned from past interactions. New preferences can be learned
from the user-item bipartite graph constructed using the newly observed
interactions. In DIL, we design an Information Extraction Module to extract
historical preferences from the existing model. Then we blend the historical
and new preferences in the form of node embeddings in the new graph, through a
Disentanglement Module. The essence of the disentanglement module is to
decorrelate the historical and new preferences so that both can be well
captured, via carefully designed losses. Through experiments on three benchmark
datasets, we show the effectiveness of DIL in capturing dynamics of useritem
interactions. We also demonstrate the robustness of DIL by attaching it to two
base models - LightGCN and NGCF.
