Walmart Apollo, LLC

Kamiya Motwani

Kannan Achan

Sushant Kumar

Evren Korpeoglu

Vidya Sagar Kalidindi

Saurabh Agrawal

Soumya Wadhwa

Rahul Ramkumar

Da Xu

Chuanwei Ruan

Venugopal Mani

Behzad Shahrasbi

Prashant Chandrakant Saundade

Durga Deepthi Singh Sharma

Sushant Pralhad Joshi

Derrick Lagomarsino

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: determining a list of possible substitutes for an item when the item is out of stock; training a machine learning algorithm, using labeled training data as input and a list of possible substitutes for the item as output; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of possible substitutes; determining a respective historical substitution score for each possible substitute; determining a respective final score for each possible substitute comprises using at least one or more rectifiers having ReLU non-linearity; ranking each possible substitute; storing a selection of the highest ranked possible substitute as additional training data with the labeled training data; and re-training the machine learning algorithm. Other embodiments are disclosed herein.

Systems and methods for determining substitutions

A recommender system can include a defender computing device that is configured to obtain customer interaction data characterizing customer interactions with an ecommerce marketplace. The defender computing device can also be configured to determine an item recommendation based on the customer interaction data using a trained differentially private recommendation model and send the item recommendation to the customer. The trained differentially private recommendation model is more likely to determine the same item recommendation after poisoned data is injected into the customer interaction data than a recommendation model that is not privately trained.

Methods and apparatuses for defending against data poisoning attacks in recommender systems

This application relates to apparatus and methods for automatically detecting attacks to advertisement systems. In some examples, a computing device trains a machine learning process based on a training dataset. The training dataset may be an identified portion of a website session dataset that includes a lower percentage of malicious data caused by attacks than other portions, or may include no malicious data. Once trained, the computing device generates features from a website session dataset for a customer, and applies the trained machine learning process to the generated features to detect malicious data within the website session dataset for the customer. Further, the computing device may filter the website session data to remove the detected malicious data, and may store the filtered website session data within a data repository. The computing device may provide the filtered website session data to a recommendation system to generate item recommendations for the customer.

Methods and apparatus for automatically detecting data attacks using machine learning processes

A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Methods and apparatuses for evaluating substitutions made during order fulfillment processes

This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Methods and apparatus for item substitution

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: determining a list of substitutes for an item; determining qualities for each substitute; determining a similarity score for each substitute; determining a historical substitution score for each substitute; determining a final score for each substitute using the similarity score for each substitute and the historical substitution score for each substitute; ranking each substitute based upon the final score for each substitute; facilitating a display, on a user interface of a user device, of a highest ranked substitute; receiving, from the user interface of the user device, a selection of the highest ranked substitute; and after receiving the selection of the highest ranked substitute, substituting the highest ranked substitute for the item of the list of items. Other embodiments are disclosed herein.

Growing community of inventors

Sunnyvale, CA, United States of America

Kamiya Motwani