Identifying shortage items from a retail environment in online marketplaces

A system for identifying items likely stolen from a physical retail environment in an online marketplace, the system comprising:

• a checkout station having at least one scanning device and a point of sale (POS) terminal, the checkout station being configured to scan items during a checkout process and generate transaction data upon completion of the checkout process;
• item detection sensors near an exit of a physical retail environment configured to detect item identification tags fixed to the items that leave the physical retail environment;
• and
• a computer system in communication with the checkout station and the item detection sensors, the computer system configured to identify items likely stolen from the physical retail environment in an online marketplace, the computer system configured to perform operations comprising:
• receiving, from the item detection sensors, item data indicating the items detected by the item detection sensors as leaving the physical retail environment, wherein the item data includes at least, for each item in the item data, an item identifier, an item type, and a timestamp at which the item was detected as leaving the physical retail environment;
• receiving, from the checkout station, the transaction data for checkout processes that have been completed at the checkout station;
• identifying a subset of items in the item data that do not match items in the transaction data as an item shortage in the physical retail environment;
• grouping items in the subset of items into a cluster based on a determination that the grouped items have timestamps within a threshold amount of time from each other;
• retrieving, from a server system hosting an online marketplace, seller listing data for the online marketplace, wherein the seller listing data comprises groups of items offered for sale in the online marketplace that are each associated with a different one of a plurality of online seller profiles;
• comparing the cluster to each of the groups of items associated with the plurality of online seller profiles to determine cluster similarity scores for each of the groups of items;
• identifying, based on the cluster similarity scores, a particular group of items and a particular corresponding seller profile as having a greatest likelihood of listing the cluster of items in the online marketplace;
• and
• returning output in response to identifying the particular group of items and the particular corresponding seller profile that, when transmitted to a user computing device, causes the user computing device to perform an action based on the particular group of items as likely being stolen from the physical retail environment.