Walmart Apollo, LLC

Cun Mu

Binwei Yang

Zheng Yan

Behzad Ahmadi

Omer Ovenc

Jun Zhao

Guang Yang

Theban Stanley

Rajyashree Mukherjee

Saumya Agarwal

Jungwoo Han

Yu Wang

Dianhui Zhu

Ayush Parshotam Ruchandani

Ying Michelle Sun

Xinyi Liu

Varun Joshi

A method including automatically determining, by a machine learning model trained based at least in part on sample items stored in a sample database, a query embedding vector for a query image of a query item. The method further can include determining, based on a respective embedding distance between the query image of the query item and a respective image of each of the sample items, neighboring items from among the sample items. The respective embedding distance can be calculated based on the query embedding vector for the query image and a respective embedding vector for the respective image of each of the sample items. Each of the sample items can include the respective image and at least one respective item label. The method also can include determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items. The method additionally can include determining a query item label of the query item based on a weighted majority vote by the neighboring items via the respective normalized weight for the each of the neighboring items. The method further can include upon determining that the query item label of the query item is different from a first item label of the query item. storing the query item with the query item label in a product database. The method also can include selectively updating the sample items stored in the sample database from items in the product database. In addition, the method can include re-training the machine learning model based at least in part on the sample items in the sample database, as updated. Other embodiments are disclosed.

System and method for determining item labels based on item images

A result ranking system can include a computing device that is configured to obtain a plurality of items based on a query and a value corresponding to each item of the plurality of items. The computing system can also be configured to retrieve a value distribution corresponding to the query and generated based on customer interaction data. The computing system can also, for each item of the plurality of items, determine a feature value based on the value distribution and the corresponding value of the item. The computing system can be further configured to transmit the plurality of items to a customer device for display in an arrangement based on the corresponding feature value.

System, method, and computer readable medium for improving ecommerce search ranking via query-price affinity values

A method including pre-screening second images from a database for a search result based on a first image and also based on respective substring distances between first binary substrings for the first image and respective second binary substrings for each of the second images. The method further can include after the pre-screening, determining a respective image distance for each third image of third images of the search result, wherein the respective image distance is between the first image and the each third image of the third images of the search result, and after determining the respective image distance for each third image of the third images of the search result, when the respective image distance for a particular one of the third images is greater than a predetermined image distance threshold, removing the particular one of the third images from the search result before the search result is displayed on a user device requesting the search result. Other embodiments are disclosed.

Image searching using a full-text search engine

A method including determining neighboring items from among sample items based on a respective embedding distance between a query image of a query item and a respective image of each of the neighboring items. The each of the sample items can comprise the respective image and at least one respective item label. The method further can include determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items. The method also can include determining a query item label of the query item based on a weighted majority vote by the neighboring items via the respective normalized weight for the each of the neighboring items. Other embodiments are disclosed.

This application relates to systems and methods for facilitating online search based on offline transaction data. In some examples, a disclosed system includes a computing device that is configured to: obtain an online search request that is submitted by a user to an online retailer; generate an online search result comprising a plurality of items that match the query and are available at a first physical retail store associated with the online retailer; generate online search data based on the online search request and the online search result; obtain in-store purchase data of the user, wherein the in-store purchase data comprise at least one offline transaction initiated by the user at one or more physical retail stores associated with the online retailer for purchasing at least one item of the plurality of items within a predetermined time period after generating the online search result; generate, for the query and for each of the at least one item purchased offline, an order feedback signal by associating the in-store purchase data with the online search data; and perform an online search based on the order feedback signal, in response to the query from any user.

Systems and methods for facilitating online search based on offline transactions

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving images at an electronic platform; generating, using a fingerprinting network associated with a neural network architecture, feature embeddings corresponding to the images; generating, using the fingerprinting network, codes based on the feature embeddings; storing the codes in one or more databases; receiving, using a search component associated with the electronic platform, a non-compliant image comprising non-compliant content; and searching the codes in the one or more databases to identify one or more of the images that include non-compliant content similar to, or the same as, the non-compliant content included in the non-compliant image. Other embodiments are disclosed herein.

Systems and methods for identifying non-compliant images using neural network architectures

A method including bit-operation and sub-code/substring filtering for image searching using a full-text search engine. The method can include determining a first binary vector comprising first binary substrings for a first image. The method also can include obtaining a respective second binary vector comprising second binary substrings for each of second images from a database. The method additionally can include determining a respective substring distance for each of the binary substring for each of the second images. The respective substring distance can be between at least a pair of a first binary substring of the first binary substrings of the first binary vector and a respective corresponding second binary substring of the second binary substrings of the respective second binary vector for each of the second images. In some embodiments, the method further can include after determining the respective substring distance for each of the binary substring for each of the second images, when the respective substring distance for one or more of the second images is not greater than a predetermined substring distance threshold, including the one or more of the second images in a search result. The method also can include determining a respective image distance for each respective third image of the search result, the respective image distance being between the first image and the each respective third image of the search result. The method additionally can include after determining the respective image distance for the each respective third image of the search result, when the respective image distance is greater than the predetermined image distance threshold, culling the each respective third image from the search result. Other embodiments are disclosed.

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Jersey City, NJ, United States of America

Cun Mu