Walmart Apollo, LLC

Zhenrui Wang

Onur Gungor

Sreenivasa Prasad Sista

Sachin Adlakha

Ajit Varghese Puthenputhussery

Alessandro Magnani

Nikita Maple Sudan

Rajyashree Mukherjee

Nitin Shailesh Baliga

Isha Ghai

Manasa Swamy

Robert Wayne Macy, Iii

Jiahao Zhang

Siyue Gao

Rini Vipinchandra Joshi

Nimit Kamlesh Desai

Sapeksha Virinchi Vemulapati

Shubham Vishwanath Barhate

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

This application relates to apparatus and methods for applying trained machine learning models to determine an item's relevance to a search query. In some examples, a query and data identifying a plurality of items are received. Item attributes for each of the plurality of items are obtained, and features are generated based on the item attributes. Further, a score is generated for each item by applying a trained machine learning model to the corresponding features and the query. Matching attributes are determined for each of the plurality of items based on the corresponding item attributes and the query, and the score of each of the plurality of items is adjusted based on the matching attributes. Further, the ranking data is generated based on the adjusted score of each of the plurality of items. The ranking data may be transmitted to a web server for display of the items.

Methods and apparatus for automatically ranking items in response to a search request

This application relates to apparatus and methods for ranking search results. In some examples, a computing device receives item data identifying items for a search query provided by a user. The computing determines, for each item, a first value based on a relevance of the item to the search query. The computing device also determines user engagement data for the user, where the user engagement data identifies engagements by the user on a webpage. The computing device determines, for each item, a second value based on the user engagement data. In some examples, the computing device determines the first values and second values by executing one or more machine learning models. The computing device further determines a ranking of the plurality of items based on the first values and the second values. The computing may transmit the ranking of the plurality of items, such as to a web server.

Methods and apparatus for automatically providing a ranking of items in response to a search request

An e-commerce search ranking system is provided and generally includes a server (e.g., application server), a database, and a customer computing device. The server can obtain user search query listings associated with a search term of users' search sessions to determine user engagement of the items. The server also determines that the items, and any items appearing before the items in each user search query listing, were examined. The server can then aggregate the information in the database. The server can also determine additional search terms related to the search term of the users' search session, and obtain user search query listings for the additional search terms. The server can determine user engagement for items appearing in those user search query listings, and can aggregate that information in the database as well. The aggregated information can be used to determine a final item ranking for a search request inquiry.

Method and apparatus for ecommerce search ranking

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Fremont, CA, United States of America

Zhenrui Wang