Walmart Apollo, LLC

Freshworks, Inc.

Sachin Adlakha

Zhenrui Wang

Ajit Varghese Puthenputhussery

Alessandro Magnani

Nikita Maple Sudan

Manasa Swamy

Rudresh Veerabhadraiah

Sapeksha Virinchi Vemulapati

Charan Kumar

Isha Ghai

Siyue Gao

Extracting key-value attributes from unstructured data includes a parser receiving a first incident comprising key-value attributes and a second incident comprising key-value attributes, and parsing the first incident for the key-value attributes and the second incident for key-value attributes. A machine learning (ML) model perform a pairwise comparison of one or more common key-value attributes associated with the first incident and the second incident. A cosine similarity module computes a cosine similarity between the one or more common key-value attributes to generate a score for each of the one or more common key-value attributes associated with the first incident and the second incident, and generates a final score between the first incident and the second incident by averaging all cosine similarity scores computed for each of the one or more common key-value attributes.

Key-value information extraction from unstructured data

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

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Santa Clara, CA, United States of America

Sachin Adlakha