Coupang, Corporation

Pankesh Bamotra

Gil Ho Lee

Sungho Kim

Min Hyun Park

Nishant Agrawal

Chan Seo

Emily Xiaoyun Hua

Ashutosh Pendse

Systems and methods are provided for categorizing products using AI. One method comprises retrieving initial training data including products associated with one or more categories; pre-processing the initial training data to generate synthesized training data; generating a hierarchical model using the synthesized training data, the hierarchical model containing at least two layers of nodes below a root node; receiving information associated with a first uncategorized product; and receiving a request to predict a set of N categories with the highest N total probability scores. The method may further comprise predicting, using the hierarchical model, N categories of the first uncategorized product, by calculating total probability scores, and determining the N categories with the highest N total probability scores; sorting the first uncategorized product into the N categories associated with the nodes from the first and second layers having the highest total probability scores; and displaying the sorted first uncategorized product and its associated N categories on a user device associated with a user.

Computerized systems and methods for product categorization using artificial intelligence

One aspect of the present disclosure is directed to a computer-implemented system for streamlined product searching configured to receive a search query comprising at least one keyword; determine a product type; retrieve a first record of products associated with the product type; determine whether the search query meets a condition; and if the search query meets the condition: determine, a plurality of label values, and for each label value: generate a second record of products associated with the label value; determine whether each product in the second record meets a condition; deleting each product that meets the condition from the second record; ranking each remaining product in each second record; display the plurality of label values for selection; receive a selected label value; and display at least a portion of the remaining ranked products associated with the selected label value.

Systems and methods for providing intelligent multi-dimensional recommendations during online shopping

Systems and methods are provided for categorizing products using Al. One method comprises retrieving initial training data including products associated with one or more categories; pre-processing the initial training data to generate synthesized training data; generating a hierarchical model using the synthesized training data, the hierarchical model containing at least two layers of nodes below a root node; receiving information associated with a first uncategorized product; and receiving a request to predict a set of N categories with the highest N total probability scores. The method may further comprise predicting, using the hierarchical model, N categories of the first uncategorized product, by calculating total probability scores, and determining the N categories with the highest N total probability scores; sorting the first uncategorized product into the N categories associated with the nodes from the first and second layers having the highest total probability scores; and displaying the sorted first uncategorized product and its associated N categories on a user device associated with a user.

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Bellevue, WA, United States of America

Pankesh Bamotra