Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
https://arxiv.org/abs/1911.08538
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A persona-driven SBRS framework learns unsupervised user personas from an LLM-initialized heterogeneous KG and incorporates them into data-driven sequential recommenders, reporting consistent gains over session-history baselines on Amazon Books and Movies & TV.
citing papers explorer
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Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
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Leveraging LLMs and Heterogeneous Knowledge Graphs for Persona-Driven Session-Based Recommendation
A persona-driven SBRS framework learns unsupervised user personas from an LLM-initialized heterogeneous KG and incorporates them into data-driven sequential recommenders, reporting consistent gains over session-history baselines on Amazon Books and Movies & TV.