Recommender Systems
Assembly Line
How Instacart Uses Machine Learning to Suggest Replacements for Out-of-Stock Products
One of Instacartâs key challenges is predicting product availability without real-time inventory data. Our machine-learning model prompts replacement suggestions if a product appears unavailable when an Instacart customer shops. This replacement model also assists Instacart shoppers in selecting the best replacements during their shopping trips.
Our model uses a Siamese network that leverages identical weights to simultaneously process two different input vectors, creating output that can be easily compared. This configuration mirrors the classic âtwo-towerâ architecture prevalent in recommendation and search ranking applications. The product layer consolidates the four types of features mentioned above into an embedding representation for a product. The model employs a BERT-based sentence embedding layer to process product name text features, and embedded representations for high-cardinality categorical features are learned from scratch during model training.