xiangwang1223/knowledge_graph_attention_network

Health v2 · maintenance only

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1164 stars · 333 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensorFlow implementation of KGAT (Knowledge Graph Attention Network), a KDD 2019 paper on knowledge-aware personalized recommendation. It models high-order connectivity in collaborative knowledge graphs using graph attention networks to improve recommendation with item side information.

Use cases

• reproduce KGAT results from the KDD 2019 paper
• build a knowledge-graph-aware recommender system
• experiment with graph attention networks for recommendation
• train recommenders on Yelp2018, Amazon-book, or Last-fm datasets
• study embedding propagation over collaborative knowledge graphs
• compare knowledge-aware recommendation baselines

When to choose

• you need a research baseline for knowledge-aware recommendation
• you want to reproduce or extend the KGAT paper
• your dataset includes a knowledge graph with item relations
• you are studying high-order connectivity in recommender systems

When to avoid

• you need a production recommender system with active maintenance
• you require modern TensorFlow 2.x or PyTorch support
• you want a plug-and-play recommendation library rather than research code
• you have no knowledge graph available for your items

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning python recommender-system knowledge-graph graph-neural-networks graph-attention-networks kdd2019 tensorflow collaborative-filtering research-code recommender-systems knowledge-graphs

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