Machine-learning based generation of text style variations for digital content items


An online system generates a set of content item variations for a reference content item that include different styles of text for the content item. The different styles of text are generated by applying machine-learned style transfer models, for example, neural network based models to reference text of the reference content item. The text variations retain the textual content of the reference text but are synthesized with different styles. The online system can provide the content item variations to users on an online experimental platform to collect user interaction information that may indicate how users respond to different styles of text. The online system or the content providers can effectively target users with content items that include the style of text the users respond to based on the collected information.


Recommended citation:

  title={Machine-learning based generation of text style variations for digital content items},
  author={Lundin, Jessica and Schoppe, Owen Winne and Han, Xing and Sollami, Michael Reynolds and Lonsdorf, Brian J and Ross, Alan Martin and Woodward, David J and Rohde, Sonke},
  month=aug # "~4",
  publisher={Google Patents},
  note={US Patent App. 17/163,162}