# GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

arXiv: https://arxiv.org/abs/2008.13517 · HTML: https://mlsanity.com/paper/2008.13517/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 30 of 42)

- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation](https://mlsanity.com/paper/2110.08727/) (2021)
- [CaT: Balanced Continual Graph Learning with Graph Condensation](https://mlsanity.com/paper/2309.09455/) (2023)
- [Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces](https://mlsanity.com/paper/2211.17068/) (2022)
- [Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs](https://mlsanity.com/paper/2304.10668/) (2023)
- [Continual Learning on Graphs: Challenges, Solutions, and Opportunities](https://mlsanity.com/paper/2402.11565/) (2024)
- [Continual Graph Learning: A Survey](https://mlsanity.com/paper/2301.12230/) (2023)
- [Towards Robust Graph Incremental Learning on Evolving Graphs](https://mlsanity.com/paper/2402.12987/) (2024)
- [Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning](https://mlsanity.com/paper/2402.16374/) (2024)
- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [Topology-aware Embedding Memory for Continual Learning on Expanding Networks](https://mlsanity.com/paper/2401.13200/) (2024)
- [Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks](https://mlsanity.com/paper/2403.05185/) (2024)
- [ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table](https://mlsanity.com/paper/2104.08542/) (2021)
- [Continual Learning on Graphs: A Survey](https://mlsanity.com/paper/2402.06330/) (2024)
- [A Topology-aware Graph Coarsening Framework for Continual Graph Learning](https://mlsanity.com/paper/2401.03077/) (2024)
- [PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation](https://mlsanity.com/paper/2312.14439/) (2023)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems](https://mlsanity.com/paper/2409.05878/) (2024)
- [GCAL: Adapting Graph Models to Evolving Domain Shifts](https://mlsanity.com/paper/2505.16860/) (2025)
- [TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2104.08419/) (2021)
- [On the Limitation and Experience Replay for GNNs in Continual Learning](https://mlsanity.com/paper/2302.03534/) (2023)
- [GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://mlsanity.com/paper/2406.08229/) (2024)
- [GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs](https://mlsanity.com/paper/2311.17410/) (2023)
- [Node Duplication Improves Cold-start Link Prediction](https://mlsanity.com/paper/2402.09711/) (2024)
- [NC-NCD: Novel Class Discovery for Node Classification](https://mlsanity.com/paper/2407.17816/) (2024)
- [Lightweight Compositional Embeddings for Incremental Streaming Recommendation](https://mlsanity.com/paper/2202.02427/) (2022)
- [Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems](https://mlsanity.com/paper/2209.01143/) (2022)
- [Efficient and Robust Continual Graph Learning for Graph Classification in Biology](https://mlsanity.com/paper/2411.11668/) (2024)
- [Enhancing Graph Contrastive Learning with Node Similarity](https://mlsanity.com/paper/2208.06743/) (2022)
- [AGALE: A Graph-Aware Continual Learning Evaluation Framework](https://mlsanity.com/paper/2406.01229/) (2024)

## Abstract (arXiv)

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.