Walmart Apollo, LLC

Kaushiki Nag

Kannan Achan

Sushant Kumar

Venkata Syam Prakash Rapaka

Shirpaa Manoharan

Lalitesh Morishetti

Rajagopal Manda

Rahul Ramkumar

Vidya Sagar Kalidindi

Surya Yalla

A method including training a logistic regression model to create a trained model to provide probabilities of users clicking on emails of one or more email campaigns within each of multiple different time periods. Input predictor variables of the logistic regression model include (i) user feature data including personal user features and online activity history for users in the multiple different time periods and (ii) email feature data including sent times and item category data for multiple different emails in the one or more email campaigns. Output dependent variables of the logistic regression model include responses by the users to the one or more email campaigns. The method also includes triggering sending a first email of the one or more email campaigns to a first user of the users at a selected time period based at least in part on the trained model. Other embodiments are disclosed.

Automatic personalized email triggers

The disclosed subject matter relates to a system and method for personalizing customer experience at a retailer's physical location in order to increase sales and customer satisfaction. The personalization is based upon classification of customer's online interaction with the retailer. Upon detecting the customer's presence at the retailer's physical location, data of the customer's online interactions is retrieved and classified based on the type of online interactions and temporal characteristics. Push content is transmitted to the customer, the push content being based upon at least the classification and data associated with retailer's physical location.

Methods and apparatus for providing context aware personalized in-store customer experience

A method including training a recurrent neural network model to create a trained model based at least in part on: (a) first images associated with first items on a website, (b) first search terms used by users of the website to search for the first items on the website, and (c) personal features of the users. The method also can include receiving an input image that was uploaded by a current user. The method additionally can include obtaining a user encoded representation vector for the current user based on a set of personal features of the current user. The method further can include generating an image encoded representation vector for the input image. The method additionally can include deriving search terms that are personalized to the current user for the one or more items depicted in the input image, using the trained model and based on the user encoded representation vector for the current user and the image encoded representation vector for the input image. Other embodiments are disclosed.

Automatic personalized image-based search

A basket-aware recommendation system can include a computing device configured to receive an indication of a navigation from a user device. The indication includes a user identifier and a set of basket items. The computing device is configured to generate a set of eligible items based on the set of basket items. The computing device is configured to generate a feature representation based on the corresponding item category count within the set of eligible items and select items of the set of eligible items within the corresponding item category. The computing device is configured to compute a number of category display items based on the potential click value and the normalized relevance value. The computing device is configured to transmit the number of category display items for each category of the set of item categories to the user device for display on a user interface.

Systems and methods for generating basket-aware item recommendations based on database entry categories

A system can include one or more processing modules and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processing modules and perform receiving, from an electronic device, a search query from a user of a plurality of users; processing first data; and facilitating displaying a set of items. Processing the first data can comprise determining one or more keywords by capturing the one or more keywords during a time window; creating a feature set of second data associated with at least a portion of the plurality of users; determining a set of items of the item set as being based at least in part on an item vector representation and a keyword vector representation; determining a respective purchase probability associated with each item of the set of items of the item set; ranking the set of items.

Systems and methods for webpage personalization

This application relates to apparatus and methods for automatically determining and providing digital recommendations including sponsored items at optimized positions. In some examples, a computing device receives a recommendation request. In response, the computing device obtains a plurality of items based on the user data. The plurality of items includes a plurality of relevant items and a plurality of sponsored items. The computing device then determines a total profit for a combination of the plurality of items by injecting the plurality of promotional items at different position amongst the plurality of relevant items. The plurality of items are presented to the user based at least in part on the total profit and the corresponding combination.

Systems and methods for providing sponsored recommendations

This application relates to apparatus and methods for providing recommended items to advertise. In some examples, a computing device determines a plurality of first values for a corresponding plurality of first items based on the user's engagement with each of the first items. The computing device may then determine a subset of the plurality of first items based on the first values. The computing device may receive a search request and determine a plurality of second values for a plurality of second items based on the search request. The computing device may determine a plurality of third values for the subset of items based on the plurality of second values for the plurality of second items and the user's engagement with each of the subset of items. The computing device may determine the recommended items based on the plurality of second values and the plurality of third values.

Methods and apparatus for generating item recommendations based on real-time inference of machine learning models

This application relates to apparatus and methods for automatically determining and providing, for a given anchor item, a ranking of items. The ranking may include sponsored items. In some examples, a computing device receives a request for items for an anchor item. The computing device determines a relevancy of a plurality of recommended items and sponsored items. The computing device also determines a cost value for the sponsored items. The computing device determines ranking values for the plurality of recommended items and sponsored items based on the relevancy values and the cost values, and ranks the items based on the ranking values. In some examples, the computing device updates the final item ranking based on the application of one or more rules. The computing device transmits the final item ranking to a web server. The web server displays advertisements for the items in ranked order.

Methods and apparatus for electronically determining item advertisement recommendations

A method including tracking first data including responses by users to email campaigns. The method also can include generating a user feature set for the users, the user feature set including personal features of each of the users and online activity history for each of the users across a first set of time periods. The method additionally can include generating an email feature set for the emails in the email campaigns. The method further can include training a logistic regression model to create a trained model using the first data, the user feature set, and the email feature set to provide a probability of a user of the users clicking on an email of the emails within each time period of a second set of time periods. The method additionally can include triggering sending a first email of the email campaigns to a first user of the users at a selected time period of the second set of time periods based at least in part on the trained model. Other embodiments are disclosed.

In some embodiments, the method can comprise receiving one or more clicks on one or more items by a user during a browse session, measuring a distance between the one or more items, and determining a relationship between the one or more items based at least in part on the distance. In various embodiments, the method can further comprise clustering the one or more items based at least in part on the relationship into one or more clusters and presenting to the user a recommendation. In a number of embodiments, the recommendation can comprise at least one of one or more search terms related to at least one cluster of the one or more clusters or a set of items related to the at least one cluster of the one or more clusters, the set of items comprising at least a portion of the one or more items. Other embodiments of related methods and systems are also provided.

Systems and methods for automated recommendations

In some embodiments, a method can comprise receiving a search query from a search by a user during a browse session, receiving one or more items from an item database in response to the search query, and receiving one or more previous search queries from a search database, the one or more previous search queries related to the search query. In many embodiments, the method can further comprise determining a purchase probability associated with a first item of the one or more items based at least in part on a first item score for the first item, ranking the one or more items based at least in part on the purchase probability associated with the first item of the one or more items, and facilitating display of the ranking of the one or more items. Other embodiments of related methods and systems are also provided.

Growing community of inventors

Santa Clara, CA, United States of America

Kaushiki Nag