GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

Abstract

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.

Accepted by CIKM2020 Applied Research Track

References in corpus (13)

• Distilling the Knowledge in a Neural Network
• Overcoming catastrophic forgetting in neural networks
• Inductive Representation Learning on Large Graphs
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Neural Message Passing for Quantum Chemistry
• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Graph Convolutional Matrix Completion
• Representation Learning for Attributed Multiplex Heterogeneous Network
• Reinforced Continual Learning
• GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
• Position-aware Graph Neural Networks
• On the Equivalence between Positional Node Embeddings and Structural Graph Representations

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• A Critical Study on Data Leakage in Recommender System Offline Evaluation
• Dynamically Expandable Graph Convolution for Streaming Recommendation
• Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
• Topology-aware Embedding Memory for Continual Learning on Expanding Networks
• GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
• Modeling Dual Period-Varying Preferences for Takeaway Recommendation
• Efficient and Robust Continual Graph Learning for Graph Classification in Biology
• TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
• ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table
• Online Continual Learning in Image Classification: An Empirical Survey