MERGING DATA FROM VARIOUS CONTENT DOMAINS TO TRAIN A MACHINE LEARNING MODEL TO GENERATE PREDICTIONS FOR A SPECIFIC CONTENT DOMAIN

Methods and systems are described herein for merging datasets from multiple content domains for training a prediction model to predict a solution for a request related to a specific content domain. A dataset for a content domain may include requests and solutions organized as groups. For example, a...

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Bibliographic Details
Main Authors Simha, Anirudha, Bernard, Nimesh, Su, Ricky, CHI, Alison, Goldstein, Cosette, Tucker, Remel
Format Patent
LanguageEnglish
Published 15.12.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Methods and systems are described herein for merging datasets from multiple content domains for training a prediction model to predict a solution for a request related to a specific content domain. A dataset for a content domain may include requests and solutions organized as groups. For example, a dataset for a first content domain may include a first group having (a) a first set of requests (e.g., questions or queries) related to a first topic, and (h) a solution (e.g., an answer) associated with the first set of requests. The datasets of different content domains are analyzed based on context-based vector representations of the requests or solutions to determine the groups that are similar and merge those similar groups into a single merged group. A prediction model is trained with the merged groups for obtaining a prediction of a solution to any given request.
Bibliography:Application Number: US202117342732