  Recommender systems now consume large-scale data and play a significant role
in improving user experience. Graph Neural Networks (GNNs) have emerged as one
of the most effective recommender system models because they model the rich
relational information. The ever-growing volume of data can make training GNNs
prohibitively expensive. To address this, previous attempts propose to train
the GNN models incrementally as new data blocks arrive. Feature and structure
knowledge distillation techniques have been explored to allow the GNN model to
train in a fast incremental fashion while alleviating the catastrophic
forgetting problem. However, preserving the same amount of the historical
information for all users is sub-optimal since it fails to take into account
the dynamics of each user's change of preferences. For the users whose
interests shift substantially, retaining too much of the old knowledge can
overly constrain the model, preventing it from quickly adapting to the users'
novel interests. In contrast, for users who have static preferences, model
performance can benefit greatly from preserving as much of the user's long-term
preferences as possible. In this work, we propose a novel training strategy
that adaptively learns personalized imitation weights for each user to balance
the contribution from the recent data and the amount of knowledge to be
distilled from previous time periods. We demonstrate the effectiveness of
learning imitation weights via a comparison on five diverse datasets for three
state-of-art structure distillation based recommender systems. The performance
shows consistent improvement over competitive incremental learning techninques.
