Our Kumo.AI blog post on Hybrid GNNs is out. https://lnkd.in/gpzr8TiF We have seen success with our Hybrid GNNs across several marketplace problems. Hybrid GNNs are a novel innovation at Kumo.AI because they model both repeated and explorative user interactions within a single GNN framework. The GNN automatically infers whether a user tends to do repeat purchases or tends to be more exploratory (ie buying new unseen items) and learns a repetition scalar per user. What we love about this approach is that it takes away from the standard recommendation systems architecture where you have multiple candidate generators - some for new unseen items, some for repeat behavior and then have to combine them in a final multi-objective optimization framework. Such systems which are a combination of multiple systems are hard to maintain, re-train and operationalize. On the Kaggle H&M challenge where most winning teams usually have approaches that require large amounts of feature engineering and the final system is a complex ensemble of approaches needing months to build, the Kumo.AI approach which is a 3 line predictive query that runs a Hybrid GNN under the hood is top 1%. Thanks Matthias Fey and Weihua Hu for the algorithms and innovation.
Congratulations team
Professor at University of New Hampshire
7moLook, Pooja Oza!