Product Recommendation Engine
ProprietaryActivePrivate eCommerce client · Data scientist
A hybrid recommendation engine for an eCommerce platform, built and deployed with Databricks Model Serving and measured in production. Client and data specifics withheld.
This work runs on proprietary client data, so everything here is intentionally vague. The client, datasets, and specific results are withheld; the lessons learned are where I go deeper on the effort itself.
Not everything I build can be written up in detail. This engine runs on proprietary customer and catalog data, so the client, the datasets, and the specific results stay private. What I can share is the shape of the work, because the shape is the point.
The problem
An eCommerce platform lives or dies on whether customers find the right product before they lose interest. No single recommendation strategy handles that well on its own: collaborative signals know nothing about new products and new customers, and content-based signals never learn from behavior. The practical answer is a hybrid, and hybrids are harder to build, deploy, and evaluate.
The approach
Hybrid recommendation models built in Databricks, combining behavioral and content signals, with the model lifecycle managed through MLflow. Deployment runs on an optimized Databricks Model Serving solution, so recommendations are served to the platform as a production service rather than a batch export.
The measurement
A recommender you don’t measure is a guess in production. The engine’s results are analyzed on an ongoing basis: how recommendations perform, where they fall short, and what that feeds back into the next iteration of the models. The specific numbers belong to the client; the discipline of the loop is the deliverable I can show.