Automated Self-Supervised Learning for Recommendation

Abstract

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model recently. However, the success of most contrastive methods heavily relies on manually generating effective contrastive views for heuristic-based data augmentation. This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation. To fill this crucial gap, this work proposes a unified Automated Collaborative Filtering (AutoCF) to automatically perform data augmentation for recommendation. Specifically, we focus on the generative self-supervised learning framework with a learnable augmentation paradigm that benefits the automated distillation of important self-supervised signals. To enhance the representation discrimination ability, our masked graph autoencoder is designed to aggregate global information during the augmentation via reconstructing the masked subgraph structures. Experiments and ablation studies are performed on several public datasets for recommending products, venues, and locations. Results demonstrate the superiority of AutoCF against various baseline methods. We release the model implementation at https://github.com/HKUDS/AutoCF.

arXiv commentAccepted by ACM The Web Conference, 2023

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Cited by in corpus (8)

• A Comprehensive Survey on Deep Graph Representation Learning
• Knowledge Graph Self-Supervised Rationalization for Recommendation
• Disentangled Contrastive Collaborative Filtering
• Diffusion-based Contrastive Learning for Sequential Recommendation
• A Comprehensive Survey on Self-Supervised Learning for Recommendation
• LightGNN: Simple Graph Neural Network for Recommendation
• Fair Augmentation for Graph Collaborative Filtering
• Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph