Large scale recommendation leveraging Graph Neural Networks



laplace is an end-to-end recommendation engine framework for large-scale graphs.

The pipeline is designed for self-supervised edge prediction on heterogenous graphs.

Pipeline Overview


  • Multi-step, hybrid recommendation pipeline:
    1. Candidate Selection:
      • Integrating LightGCN recommendations (can be ran on its own aswell)
      • Multiple, custom heuristics
      • Strategies can be mixed and matched
    2. Ranking: GraphConvolutional network prediction on candidate edges
  • Works on Heterogenous graphs
    • User based training, validation and test splitting
    • N-hop neighborhood aggregation
    • Node Features
    • Works on any number of node types
  • Advanced preprocessing of tabular data into graphs
    • Neo4j integration for better visualization and handling of large graphs. No newline at end of file

Interested in Laplace or projects like it?
Get in touch with us:

Dream Faster AI

UG (haftungsbeschränkt)

🐻 Berlin