Structure Aware Experience Replay for Incremental Learning in Graph-based Recommender Systems

Abstract

Large-scale recommender systems are integral parts of many services. With the recent rapid growth of accessible data, the need for efficient training methods has arisen. Given the high computational cost of training state-of-the-art graph neural network (GNN) based models, it is infeasible to train them from scratch with every new set of interactions. In this work, we present a novel framework for incrementally training GNN-based models. Our framework takes advantage of an experience reply technique built on top of a structurally aware reservoir sampling method tailored for this setting. This framework addresses catastrophic forgetting, allowing the model to preserve its understanding of users' long-term behavioral patterns while adapting to new trends. Our experiments demonstrate the superior performance of our framework on numerous datasets when combined with state-of-the-art GNN-based models.