Maplebear Inc.

Mark Oberemk

Brent Scheibelhut

Naval Shah

Shaun Navin Maharaj

Charles Wesley

A trained machine-learning model is used to group items in a database of an online system that represent a same core item (i.e., product) but of different attributes. The online system applies, for pairs of items from a specific chunk of the database, the machine-learning model to metadata for each pair of items and category data for each pair of items to generate a clustering score for each pair of items that indicates a likelihood that both items belong to a cluster of items that identifies the core item. The online system then requests a language model to generate a response including a list of attributes in a structured form for each item from the cluster. The online system stores, in an entry of the database associated with each item from the cluster, the list of attributes in the structured form and an identification of the cluster.

Clustering database items based on output of machine-learning model to link database items that represent the same core item but with a different stored size or form attribute

A system or a method for using machine learning to automatically route user inquiries to a retailer are presented. The system receives an inquiry from a client device associated with a user. The inquiry includes text content and an image. The system uses a natural language model to analyze the received text to identify a first category of items. The system applies the received image to an image recognition model to identify a second category of items contained in the received image. The system then identifies a retailer that carries items in at least one of the first or second category of items, and suggests the retailer to the user via the client device associated with the user. A retail associate at the retailer can then respond to the inquiry via a client device associated with the retailer.

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Toronto, Canada

Mark Oberemk