Walmart Apollo, LLC

Nitin Shailesh Baliga

Zheng Yan

Zhenrui Wang

Jun Zhao

Rajyashree Mukherjee

Nimit Kamlesh Desai

Shubham Vishwanath Barhate

Weiqi Tong

Robert Wayne Macy, Iii

Jingbo Liu

Jiahao Zhang

Rini Vipinchandra Joshi

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 cause the one or more processors to perform acts of: in response to receiving search queries at a search engine, storing search event data and ranking features in one or more databases; generating, using the search event data and the ranking features, a training dataset comprising training event samples; executing a hybrid labeling procedure that assigns labels to the training event samples based, at least in part, on individual engagement information associated with the training event samples; and training a personalized ranking model to rank search results using the training event samples and the labels. Other embodiments are disclosed herein.

Systems and methods for improving search result personalization and contextualization using machine learning models

A systems 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: receiving in-session user activity information, the in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, and a product of the products being part of a boost category; determining a respective content model prediction score for each of the products of the ranked list of the products; determining a respective discounted cumulative gain (DCG) score for each of the products of the ranked list of the products; analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products; and in response to determining that the product that is part of the boost category is to be repositioned, transmitting instructions to modify a graphical user interface to display the ranked list of the products with the product repositioned within the ranked list of the products. Other embodiments are described.

Systems and methods for altering a graphical user interface based on exploration of search query ranking

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San Jose, CA, United States of America

Nitin Shailesh Baliga