Glossary · E-commerce ML
Graph Neural Networks
also: GNN · GraphSAGE · graph convolution
Definition
Graph Neural Networks learn representations over graph-structured data by message-passing between nodes. In e-commerce cross-sell, GNNs ingest the user-item-category graph and produce recommendations that respect product hierarchy, co-purchase relationships, and session structure, outperforming flat collaborative filtering by 15-25% on business metrics.
Standard recommendation models treat the user-item matrix as a flat bipartite graph. GNNs, GraphSAGE, GCN, PinSAGE, explicitly model multi-hop relationships and aggregate information from graph neighborhoods. The architecture's strength in cross-sell is its ability to exploit complementarity structure (products in the same cart) and category hierarchy simultaneously. Pinterest's production PinSAGE serves billions of recommendations daily using a GraphSAGE variant tuned for their pin-board-user graph.
Essays on this concept
- Business Analytics
Causal Discovery in Business Data: Applying PC Algorithm and FCI to Find Revenue Drivers Without Experiments
Correlation tells you that feature usage and retention move together. It doesn't tell you which causes which, or whether a third factor drives both. Causal discovery algorithms can untangle this from observational data alone.
- E-commerce ML
Cold-Start Problem Solved: Few-Shot Learning for New Product Recommendations Using Meta-Learning
New products get no recommendations. No recommendations means no clicks. No clicks means no data. No data means no recommendations. Meta-learning breaks this loop by transferring knowledge from products that came before.
- E-commerce ML
Graph Neural Networks for Cross-Sell: Modeling the Product Co-Purchase Network at Scale
Association rules find that beer and diapers are co-purchased. Graph neural networks understand why, the underlying structure of complementary needs, occasion-based shopping, and brand affinity networks that connect products across categories.
- E-commerce ML
Real-Time Fraud Detection at Checkout: A Streaming ML Pipeline Architecture with Sub-100ms Latency
You have 100 milliseconds to decide whether a transaction is fraudulent. In that window, you need to compute 200+ features from streaming data, run inference on a model trained on 1:1000 class imbalance, and return a score that balances revenue loss against customer friction.
- E-commerce ML
Search Ranking as a Revenue Optimization Problem: Learning-to-Rank with Business Objective Regularization
E-commerce search is not Google search. When a user types 'running shoes,' the goal isn't to find the most relevant document, it's to surface the product most likely to be purchased at the highest margin. This reframes ranking as a constrained revenue optimization problem.
- E-commerce ML
Transformer-Based Product Embeddings: Outperforming Collaborative Filtering with Multimodal Representations
Collaborative filtering needs a user to buy before it can recommend. Transformer-based embeddings understand products from their descriptions, images, and the behavioral context of browsing sessions, no purchase history required.
Related concepts
Authoritative references