Walmart Apollo, LLC

Zigeng Wang

Wei Shen

Cheng Jie

Tong Yao

Jae Young Kim

Bohan Zhai

Systems and methods for generating a machine learning model to support multiple tasks for semantic retrieval, embedding and classification are disclosed. In some embodiments, a disclosed method includes: obtaining a training data set generated based on search related data and item related data associated with a website; training, based on the training data set, a machine learning model using a two-tower structure to generate an optimized set of model parameters, wherein the optimized set of model parameters minimizes a total loss function computed based on at least one of: a query classification loss, an item classification loss, and a similarity loss; and transmitting the trained machine learning model to be utilized for at least one of the following tasks: semantic item retrieval, query classification, item classification, query clustering, and item clustering.

System and method for generating a multi-task machine learning model

Systems and methods of generating keyword-specific content are disclosed. A request for including a keyword is received and the keyword is classified as one of catalog related or unrelated. When the keyword is catalog related, the keyword is categorized in a category associated with the catalog and at least one term in the keyword is categorized in a facet category associated with the catalog. A content template is obtained. The content template is a category specific template when the keyword is catalog related and a generic template when the keyword is catalog unrelated. The category specific template is populated with the at least one term at a position associated with the one of the plurality of facet categories. Responsive content including the category specific template populated with the at least one term when the keyword is catalog related and the generic template when the keyword is catalog unrelated, is transmitted.

Systems and methods for generating keyword-specific content with category and facet information

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 respective interaction metric between at least two respective keywords of a plurality of keywords; constructing, using each respective interaction metric, as determined, a graph comprising a plurality of nodes connected by at least one edge; identifying one or more clusters of nodes using the graph; optimizing each cluster of nodes of the one or more clusters of nodes; and facilitating altering a graphical user interface (GUI) of an electronic device based upon a cluster of nodes of the one or more clusters of nodes, as identified and optimized. Other embodiments are disclosed herein.

Systems and methods for keyword categorization

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 a set of keywords from a graphical user interface of an electronic device of a user; pre-processing at least one keyword of the set of keywords; receiving a hierarchical categorization; pre-processing at least one category of the hierarchical categorization; determining a respective similarity between each of the at least one keyword of the set of keywords and each of the at least one category of the hierarchical categorization; determining a respective confidence level of a most likely category in the hierarchical categorization for each of the at least one keyword of the set of keywords using the respective similarity between each of the at least one keyword of the set of keywords and each of the at least one category of the hierarchical categorization; ranking each of the at least one keyword of the set of keywords using a respective importance of each keyword of the set of keywords and the respective confidence level of the most likely category in the hierarchical categorization; and altering a respective arrangement of at least one of the at least one keyword on the graphical user interface on the electronic device of the user based on the ranking. Other embodiments are disclosed herein.

Growing community of inventors

Coventry, CT, United States of America

Zigeng Wang