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Graph Neural Networks in Recommender Systems: A Survey
With the explosive growth of online information, recommender systems pla...
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A Practical Incremental Method to Train Deep CTR Models
Deep learning models in recommender systems are usually trained in the b...
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Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network
Most modern successful recommender systems are based on matrix factoriza...
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Continual Graph Learning
Graph Neural Networks (GNNs) have recently received significant research...
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Contrastive Learning for Recommender System
Recommender systems, which analyze users' preference patterns to suggest...
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Metapath- and Entity-aware Graph Neural Network for Recommendation
Due to the shallow structure, classic graph neural networks (GNNs) faile...
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Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
Graph representation learning is gaining popularity in a wide range of a...
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GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
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.
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