Walmart Apollo, LLC

Evren Korpeoglu

Kannan Achan

Sinduja Subramaniam

Sushant Kumar

Aleksandra Cerekovic

Rahul Sridhar

Shirpaa Manoharan

Jiwen You

Da Xu

Venkata Syam Prakash Rapaka

Abhimanyu Mitra

Kamiya Motwani

Chuanwei Ruan

Sonal Bathe

Behzad Shahrasbi

Saurabh Agrawal

Soumya Wadhwa

Venugopal Mani

Durga Deepthi Singh Sharma

Vidyanand Krishnan

Hyun Duk Cho

Rahul Ramkumar

Jianpeng Xu

Lucinda Frink Newcomb

Tejal Kumar Patted

Thomas Russel Ward

Ryan Wayne Travis

Stephen Dean Guo

Rahul Iyer

Shubham Gupta

Yokila Arora

Luyi Ma

Mark Ibbotson

Divya Chaganti

Sneha Gupta

Rishi Rajasekaran

Sriram Guna Sekhar Kollipara

Prashant Chandrakant Saundade

Najmeh Forouzandehmehr

Victor Anthony Perry, Iv

Sandhya Ajit

Shirpaa Mancharan

Ahsaas Bajaj

Abhimanya Mitra

Niousha Bolandzadeh Fasaie

A system including 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: outputting, by a machine-learning model, a probability that a user will re-order two or more items at a present time; determining the two or more items to recommend to the user based on the probability exceeding a predetermined threshold that the user will re-order the two or more items at the present time; sending instructions to display the two or more items to the user, wherein the user interface comprises a single-click option to add to an electronic cart the two or more items; and after receiving the single-click option from the user interface, adding the two or more items to the electronic cart. Other embodiments are disclosed.

Single-select predictive platform model

A computer-implemented method including automatically generating predictions of a respective number of items that a user is likely to reorder in each of groups of the items that a user has ordered historically. The method also can include ranking the groups based on the predictions of the respective number of the items the user is likely to reorder in each of the groups. The method additionally can include transmitting for display to the user a user interface including the groups of the items. Other embodiments are described.

Automatic item grouping and personalized department layout system and method for reorder recommendations

A system including 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, perform certain acts. The acts can include obtaining training data. The acts also can include training candidate recommendation models and an adversarial exposure model using the training data. The acts additionally can include generating recommendations based on a selected recommendation model of the candidate recommendation models. Other embodiments are described.

Generating recommendations using adversarial counterfactual learning and evaluation

A system including 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: mapping each item of multiple items in a mixed-intent basket to a respective product type code (PT code); generating a respective list of complementary product type codes from each respective PT code; generating, using a complementary item algorithm, a respective candidate set of complementary items; detecting a platform-level configuration of a platform used by an electronic device of a user; loading, using diversity rotation, the respective quantity of complementary items onto a website carousel; and displaying the website carousel, as loaded, on the electronic device of the user, wherein the website carousel is sized to fit the platform-level configuration. Other embodiments are disclosed.

Systems and methods for recommending mixed-intent basket complementary items

Systems and methods including 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: receiving a user request via a graphical user interface, the user request corresponding to a user search query for a product; determining whether a first processing machine of the system is operating in a first processing mode or a second processing mode; when the first processing machine is determined to be operating in the first processing mode, analyzing the user request via the first processing machine and using a process, to identify a candidate recommendation system to utilize by: determining a randomized strategy for one or more candidate recommendation systems based on a ratio of a number of the one or more candidate recommender systems, the randomized strategy to be stored in a collected history data; determining model parameters based on the collected history data; and determining the candidate recommendation system from the one or more candidate recommendation systems as a candidate recommendation system with a maximum value for a reward model based on the user request; processing the user request with the candidate recommendation system to identify recommended products to display to the user; and transmitting instructions to modify the graphical user interface to display the recommended products to the user.

Systems and methods for recommendation system analysis

A system including 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 functions comprising: receiving one or more vectors representing one or more types of features for a pair of items; generating, using a similarity item model of a machine learning architecture, a prediction for a similar item, wherein the similarity item model combines a pair of separately trained machine learning models; combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items; and transmitting the similar item to a first position on a carousel display of a website that concurrently displays the anchor item on the website. Other embodiments are disclosed.

Similar item recommendation framework using wide-and-deep-architecture

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 in-session user activity entered into on an initial graphical user interface (GUI) from a user electronic device of a user; pre-processing the in-session user activity to determine one or more intents of the in-session user activity; comparing the one or more intents of the in-session user activity with one or more complementary intents; and coordinating displaying a complimentary GUI on the user device of the user based on the one or more complementary intents. Other embodiments are disclosed herein.

Systems and methods for altering a graphical user interface (GUI) based on affinity and repurchase intent

A system can include 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: selectively aggregating in-session user activity of a user with historical activity data of the user into one or more respective groups based on interactions of the user with a GUI over a period of time; predicting, using a set of predictive algorithms, one or more intents of the user based on the one or more respective groups; and facilitating a display of an altered GUI on an electronic device of the user based on the one or more intents of the user, as predicted. Other embodiments are disclosed herein.

Systems and methods for altering a graphical user interface

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 acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

Systems and methods for generating basket and item quantity predictions using machine learning architectures

A system comprising one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when executed to run on the one or more processors, cause the one or more processors to perform: receiving in-session user activity entered into on a graphical user interface (GUI) from a user electronic device of a user; generating, using a predictive algorithm, a ranked list of one or more likely intents of the user to perform one or more actions on the GUI; processing the in-session user activity to determine one or more intents of the in-session user activity; comparing the one or more intents of the in-session user activity; and coordinating a display of a likely-to-be-used GUI element on the GUI. Other embodiments are disclosed herein.

A method including tracking impression response data in response to online impressions of content elements displayed to users of a website. The method also can include performing a classification of the users based on the impression response data. The method additionally can include generating a webpage of the website to comprise a content element selected from among the content elements based on the classification of a user of the users. Other embodiments are described.

Automatic resolution of the explore-exploit decision in omnichannel settings

A privacy system includes a computing device configured to obtain user transactional data characterizing at least one transaction of a user on an ecommerce marketplace and to determine a privacy vulnerability score of the user by comparing the transactional data to a user vulnerability distribution. The computing device is also configured to send the privacy vulnerability score to a personalization engine.

Methods and apparatuses for identifying privacy-sensitive users in recommender systems

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing first transaction data stored in a transaction database, the first transaction data describing first transactions for first items from first users; determining, using the first transaction data, first micro-intents associated with the first transaction data; grouping the first micro-intents into clusters; labeling each cluster of the first micro-intents with a respective label; receiving second transaction data of a user, the second transaction data describing second transactions for second items for the user; determining, using the second transaction data, second micro-intents present in the second transactions; receiving current transaction data from a user interface of an electronic device of the user; determining, using the current transaction data, that the user is expressing a current micro-intent, the current micro-intent having at least one associated label; and transmitting an instruction to display, on the user interface of the electronic device, a user interface element correlated with the at least one associated label. Other embodiments are disclosed herein.

Systems and methods for identifying and using micro-intents

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, cause the one or more processors to perform functions including: reconfiguring a webpage as an undirected graph; identifying a cyclic dependency in the undirected graph; iterating processing one or more inferences over the cyclic dependency for each pair of the nodes of the set of the nodes; breaking one or more of edges of the undirected graph; determining, based at least in part on compatibility functions of the one or more edges remaining after breaking the one or more of the edges, a probability of the webpage having exceeded a predetermined threshold to cause a user to take an action; and sending instructions to display the webpage. Other embodiments are described.

Whole page personalization with cyclic dependencies

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 functions comprising: generating one or more feature vectors for a user, the one or more feature vectors at least comprising transaction-based features and slot-based features; generating, using a machine learning architecture, a repurchase prediction for the user based, at least in part, on the one or more feature vectors; generating, using the machine learning architecture, a time slot prediction for the user based, at least in part, on the one or more feature vectors, the time slot prediction predicting a time slot desired by the user for an upcoming transaction; and executing a reservation function that facilitates reserving of the time slot for the user. Other embodiments are disclosed herein.

Systems and methods for generating time slot predictions and repurchase predictions using machine learning architectures

Growing community of inventors

Sunnyvale, CA, United States of America

Evren Korpeoglu