Walmart Apollo, LLC

Swagata Chakraborty

Yanxin Pan

Abon Chaudhuri

Abhinandan Krishnan

Aakash Mayur Mehta

Edison Mingtao Zhang

Kyu Bin Kim

Ekaterina Pirogova

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: creating an adjacency list for candidate items using a distance threshold; generating graphs of the candidate items in the adjacency list, wherein nodes of the graphs represent the candidate items, and wherein edges of the graphs represent respective predicted variant neighbor links between pairs of the candidate items; determining, using breakdown logic, first graphs of the graphs that exceed a predetermined size; performing divisive hierarchical clustering on each of the first graphs; and identifying recommended variant groups of the candidate item in the nested subclusters of the hierarchy dendrogram below the respective cut-off value. Other embodiments are described.

Scalable pipeline for machine learning-based base-variant grouping

A method including obtaining image data and attribute information of a first item in an item catalog. The method also can include generating candidate variant items from the item catalog for the first item using a combination of (a) a k-nearest neighbors approach to search for first candidate variant items based on text embeddings for the attribute information of the first item, and (b) an elastic search approach to search for second candidate variant items based on image embeddings for the image data of the first item. The method additionally can include performing respective classifications based on respective pairs comprising the first item and each of the candidate variant items to filter the candidate variant items. The method further can include determining a respective distance between the first item and each of the candidate variant items, as filtered. The method additionally can include determining one or more items in the candidate variant items, as filtered, to include in a variant group for the first item, based on a decision function using a predetermined threshold and the respective distance for the each of the candidate variant items, as filtered. Other embodiments are described.

Automatically determining items to include in a variant group

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform obtaining a set of items that have been grouped together as matching items in a group; performing an ensemble mismatch detection; performing multiple detection models on the set of items to generate respective outputs regarding mismatches; combining the respective outputs to determine whether a quantity of detected mismatches is at least a predetermined threshold; when the quantity of detected mismatches is at least the predetermined threshold, the acts also can include separating at least one of the set of items from the group; and when the quantity of detected mismatches is not at least the predetermined threshold, the acts additionally can include maintaining each item of the set of items in the group. Other embodiments are disclosed.

Mismatch detection model

Growing community of inventors

Santa Clara, CA, United States of America

Swagata Chakraborty