Tag-aware Knowledge Graph Attention Network

This is the PyTorch implementation for the paper Tag-aware Knowledge Graph Attention Network in Knowledge-Based Systems.

Environment Requirement

The code has been tested running under Python 3.6.8. The required packages are as follows:

• torch == 1.3.1
• dgl-cu90 == 0.4.1
• numpy == 1.15.4
• pandas == 0.23.1
• scipy == 1.1.0
• sklearn == 0.20.0

Run the Codes

• TKGAT

python main_tkgat.py --data_name movielens

Related Papers

• FM

  • Proposed in Fast context-aware recommendations with factorization machines, SIGIR2011.

• NFM

  • Proposed in Neural Factorization Machines for Sparse Predictive Analytics, SIGIR2017.

• BPRMF

  • Proposed in BPR: Bayesian Personalized Ranking from Implicit Feedback, UAI2009.
  • Key point:
    • Replace point-wise with pair-wise.

• ECFKG

  • Proposed in Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation, Algorithm2018.
  • Implementation by the paper authors: https://github.com/evison/KBE4ExplainableRecommendation
  • Key point:
    • Introduce Knowledge Graph to Collaborative Filtering

• CKE

  • Proposed in Collaborative Knowledge Base Embedding for Recommender Systems, KDD2016.
  • Key point:
    • Leveraging structural content, textual content and visual content from the knowledge base.
    • Use TransR which is an approach for heterogeneous network, to represent entities and relations in distinct semantic space bridged by relation-specific matrices.
    • Performing knowledge base embedding and collaborative filtering jointly.

• KGAT

  • Proposed in KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019.
  • Implementation by the paper authors: https://github.com/xiangwang1223/knowledge_graph_attention_network
  • Key point:
    • Model the high-order relations in collaborative knowledge graph to provide better recommendation with item side information.
    • Train KG part and CF part in turns.