Walmart Apollo, LLC

Microsoft Technology Licensing, LLC

Yahoo! Inc.

Kannan Achan

Sushant Kumar

Evren Korpeoglu

Hyun Duk Cho

Venkata Syam Prakash Rapaka

Stephen Dean Guo

Abhimanyu Mitra

Sinduja Subramaniam

Afroza Ali

Rahul Iyer

Kaushiki Nag

Soumya Wadhwa

Aditya Mantha

Shirpaa Manoharan

Praveenkumar Kanumala

Yokila Arora

Shubham Gupta

Rahul Ramkumar

Aysenur Inan

Aleksandra Cerekovic

Rahul Sridhar

Luyi Ma

Vidya Sagar Kalidindi

Kamiya Motwani

Ashish Ranjan

Omkar Deshpande

Nimesh Sinha

Anirudha Sundaresan

Da Xu

Shashank Kedia

Venugopal Mani

Jiwen You

Chuanwei Ruan

Sneha Gupta

Divya Chaganti

Sonal Bathe

Behzad Shahrasbi

Abhay Kashyap

Selene Xu

Mansi Ranjit Mane

Saurabh Agrawal

Erick Cantu-Paz

Jianpeng Xu

Durga Deepthi Singh Sharma

Rishi Rajasekaran

Yang Deng

Yue Xu

Basant Choudhary

Prashant Chandrakant Saundade

Ron Benson

Fang Yu

Yinglian Xie

Min Xie

Zoheb Vacheri

Rukmini Iyer

Sree Vasthav Shatdarshanam Venkata

Eren Manavoglu

Ozgur Cetin

Gaoyang Wang

Derrick Lagomarsino

Vivek Vaidyanathan

Yogananth Mahalingam

Rajagopal Manda

Sooraj Mangalath Subrahmannian

Lalitesh Morishetti

Surya Prasanna Kumar

Parth Ramesh Vajge

Swati Bhatt

Sushant Pralhad Joshi

Rakesh Agrawal

Eliot Carleton Gillum

Vidyanand Krishnan

Moises Goldszmidt

Ariel Fuxman

Mark Ibbotson

Panayiotis Tsaparas

Sriram Guna Sekhar Kollipara

Jeremy King

Rahul Ghosh

Najmeh Forouzandehmehr

Shirpaa Mancharan

Parveenkumar Kanumala

Malay Patel

Ryan Wayne Travis

Lucinda Frink Newcomb

Thomas Russel Ward

Tejal Kumar Patted

Surya Yalla

Victor Anthony Perry, Iv

Sandhya Ajit

Morteza Monemizadeh

Vimitha Manohar

Veena Santhoshi Rajasekar

Ahsaas Bajaj

Apoorv Reddy Arrabothu

Atul Kochhar

Xiaotong Suo

Abhimanya Mitra

Kailing Wang

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

Systems and methods for post transaction seasonal item recommendations are disclosed. In some embodiments, a current seasonal time window associated with a seasonal event and some seasonal product types is determined. Based on historical transaction data of the seasonal product types, a first seasonal index score is computed for each item, and a second seasonal index score is computed for each product type including one or more items. A seasonal rank score is generated for each item based on the first seasonal index score and the second seasonal index score, such that the items in the historical transaction data are ranked based on their respective seasonal rank scores. Based on the ranked items and a transaction order from a user, a list of recommended items is generated and displayed to the user.

System and method for post transaction seasonal item recommendations

A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.

Methods and systems for determining household characteristics

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 instruction that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a training procedure, labels based at least in part on price band activity data from a time period; training, using the training procedure, an affinity prediction model of a machine learning architecture; analyzing, using the affinity prediction model of the machine learning architecture, as trained, the price band activity data indicating interactions of a user with items; and generating, using the labels and the affinity prediction model of the machine learning architecture, as trained, one or more price affinity predictions for one or more items for the user. Other embodiments are disclosed herein.

Systems and methods for determining price bands and user price affinity predictions using machine learning architectures and techniques

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 is configured to train a customer understanding model to generate a preference score for substitution items. The customer understanding model generates a preference score for each of a plurality of related substitution items based on order data including data indicative of at least one item ordered and location data indicating a location of a first store. The customer understanding model ranks each of the substitution items based on the preference score. Order data is transmitted including substitution data identifying each of the substitution items and corresponding rank. Performance data associated with a set of operations implemented based on the order data and the substitution data is obtained. An updated customer understanding model is trained based on the performance data and iteratively modified based on the updated training dataset and updated performance metrics generated from second performance data.

Methods and apparatuses for recommending substitutions made during order fulfillment processes

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 operations comprising: evaluating, using a price band determination model, degrees of expensiveness of items relative to each other in item type categories; generating, using the price band determination model, price bands associated with item type categories; assigning each of the items to a respective one of the price bands associated with a respective one of the item type categories; and presenting, to one or more end-user applications, at least one other item corresponding to at least one of the price bands associated with at least one of the item type categories. Other embodiments are disclosed.

Systems and methods for generating enhanced user interfaces are disclosed. A set of candidate items responsive to a request for an enhanced interface is generated. The set of candidate items is responsive to a query or an anchor item. A composite candidate score for each candidate item is generated by a pick item scoring engine based on one or more historical interactions. A set of top-k items is selected from the set of candidate items and at least one pick item is selected from the set of top-k items. At least one enhancement interface element is selected for display in conjunction with the at least one pick item and a response to the request for the enhanced interface is transmitted that causes a receiving device to generate a user interface including the at least one pick item displayed in conjunction with the at least one enhancement interface element.

Systems and methods for query enrichment and generation of interfaces including enriched query results

In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transactional data of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of trial membership scores, based on the set of features. In some examples, each trial membership score of the plurality of trial membership scores are associated with a particular user of the set of users and characterize a likelihood of an acquisition event of the corresponding user changing from a non-member status to a trial member status.

Methods and apparatus for predicting a user acquisition event

Growing community of inventors

Saratoga, CA, United States of America

Kannan Achan