Walmart Apollo, LLC

Rahul Iyer

Kannan Achan

Stephen Dean Guo

Praveenkumar Kanumala

Soumya Wadhwa

Sushant Kumar

Shubham Gupta

Shashank Kedia

Erik C Scher

Hyun Duk Cho

Rahul Ramkumar

Aysenur Inan

Anirudha Sundaresan

Surya Prasanna Kumar

Jarod Mccormick

Srinivas Vuddagiri

Joel Cizeron

Evren Korpeoglu

Greg Nyce

Aditya Mantha

Yokila Arora

Sam Weinberger

Divya Chaganti

Sneha Gupta

Joel Herger

Benjamin Saydah

Prashant Chandrakant Saundade

Victor Anthony Perry, Iv

Sandhya Ajit

Kamilia Ahmadi

Keerthi Gopalakrishnan

Ajay Madgavkar

Lawrence Peck

A session-specific conversion determination system can include a computing device configured to receive real-time signals of an event occurring from a user device. The real-time signals include interaction parameters. The computing device is also configured to obtain a set of historical data based on the interaction parameters and a set of facets and generate a probability affinity for each facet of the set of facets by implementing a machine learning model using the interaction parameters and the set of historical data as features. The computing device is also configured to adjust a display of a set of recommended items based on the probability affinity for each facet of the set of facets and transmit the display of the set of recommended items to a user interface of the user device.

Systems and methods for personalizing recommendations using real-time signals of a user session

In some examples, at least one processor executes instructions to, during a chat session, obtain, from a computing device of a first user, session data associated with the chat session and a first user, obtain a first set of data identifying a set of price drop items associated with the chat session, and obtain a second set of data identifying a set of attribute values associated with one or more attribute features of each item type. Based on the first set of data and the second set of data, a third set of data including affinity value data for each price drop item is generated. A fourth set of data identifying a set of items each with a score is obtained. A fifth set of data is then generated based on the third set of data and the fourth set of data.

Methods and apparatus for determining item affinities associated with online chat sessions

This application relates to apparatus and methods for generating preference profiles that may be used to rank search results. In some examples, a computing device obtains browsing session data and determines items that were engaged, such as items that were viewed or clicked. The computing device obtains item property data, such as product descriptions, for the items, and applies a dependency parser to the item property data to identify portions that include certain words, such as nouns or adjectives, which are then identified as attributes. The computing device generates attribute data identifying portions of the item property data as item attributes. In some examples, the computing device applies one or more machine learning algorithms to the session data and/or search query to identify item attributes. The computing device may generate a profile that includes the item attributes, and may rank search results based on the attribute data, among other uses.

Methods and apparatus for automatically providing personalized search results

Systems and methods utilizing a classification model and a ranking model are disclosed. A user identifier is received and a user profile is generated. The user profile includes a plurality of user features. The user profile is classified into a classification using a trained classification model. The trained classification model receives a first subset of the plurality of user features. A set of communication elements is ranked using a trained ranking model. The trained ranking model receives a second subset of the plurality of user features. An electronic communication including a plurality of interface elements is generated. The plurality of interface elements includes at least one communication element selected from the ranked set of communication elements in descending ranked order. A type of the electronic communication is selected based on the classification of the user profile.

Systems and methods for benefit affinity using trained affinity models

This application relates to systems and methods for providing search results based on a primary intent. In some examples, a disclosed system includes a memory resource storing instructions; and one or more processors coupled to the memory resource. The one or more processors are configured to execute the instructions to: receive, from a user, a search query including a plurality of words, identify a plurality of intention terms from the words of the search query, compute, for each of the plurality of intention terms, a compatibility score between the intention term and a query context associated with the intention term, determine, from the plurality of intention terms, a primary intention term having a maximum compatibility score among the plurality of intention terms, and generate, based on the primary intention term, a set of intent-based search results in response to the search query, the set of intent-based search results identifying a set of items associated with the primary intention term.

Systems and methods for providing search results based on a primary intent

This application relates to apparatus and methods for automatically determining item relevancy based on textual information. In some examples, a computing device receives a search query, and a plurality of items corresponding to the search query. The computing device may identify one or more features of the search query. The computing device may generate relevancy values for each of the items based on the features of the search query, and features of each of the plurality of items. For example, the computing device may generate, for each of the items, a plurality of relevance values, each relevance value generated based on a feature of the search query and corresponding features of the item. The computing device may transmit the generated relevancy values for the plurality of items. In some examples, the computing device may rank the plurality of items based on the generated relevancy values.

System, method, and computer readable medium for automatic item rankings

In some examples, a system may be configured to generate one or more query attributes for a search query received from a computing device of a user. Additionally, the system may be configured to, based at least in part on historical data of the user including data characterizing one or more items associated with the user, generate relevant item data. In various examples, the relevant item data characterizing a set of relevant items. Moreover, the system may be configured to, based on the relevant item data, the historical data of the user and the one or more query attributes, implement a set of operations that generate a set of personalized search results associated with the search query.

Personalized ranking using deep attribute extraction and attentive user interest embeddings

A category recommender system includes a computing device configured to obtain customer information characterizing a customer's interactions on an ecommerce marketplace. The computing device is further configured to determine discovery category rankings for each category of items available on the ecommerce marketplace for the customer based on the customer information, determine repeat category rankings for each category of items available on the ecommerce marketplace for the customer based on the customer information, and determine item rankings for each item in the ecommerce marketplace based on the customer information. The computing device is further configured to merge the discovery category rankings, the repeat category rankings and the item recommendation rankings into final recommendations based on one or more predetermined merging criteria and provide the final recommendations to the customer.

Methods and apparatuses for determining personalized recommendations using customer segmentation

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 receiving a user identifier, receiving an item identifier, determining user item quantity information related to quantities of the item previously selected by the user, determining a respective household size for each user, and determining aggregate household item quantity information related to quantities of the item previously selected by an aggregate of users of the same household size. If a first threshold level of the quantity of transactions is met, a recommended quantity is based on the user item quantity information, and if not, the recommended quantity is based on the aggregate household item quantity information. The user interface of the electronic device is updated to notify the user of the recommended quantity. Other embodiments are disclosed herein.

Systems and methods for prediction of item quantity

A system and method of generating user personalized search results is disclosed. A search query including one or more words is received and a set of relevance-based search results is generated in response to the search query. One or more query attributes are generated for the search query. Historic data for a user associated with the search query is received and a set of personalized search results is generated from the set of relevance-based search results based on the query attributes and the historic data for the user. The historic data includes one or more items associated with the user.

A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of: (a) the user profile; (b) the anchor item; (c) the one or more complementary items; or (d) one or more feedbacks from the user associated with one or more prior recommendations in the session. The method additionally can include after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device. The method likewise can include after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user. Other embodiments are disclosed.

Automatically determining in real-time a triggering model for personalized recommendations

Growing community of inventors

Santa Clara, CA, United States of America

Rahul Iyer