Facebook, Inc.

Meta Platforms, Inc.

Max Christian Eulenstein

Lars Seren Backstrom

Alexander Peysakhovich

Lu Wang

Lauren Elizabeth Scissors

Jeffrey William Pasternack

David Vickrey

Prasoon Mishra

Jianfu Chen

Kritarth Anand

Polina Kuznetsova

Justin MacLean Coughlin

Austen Norment McDonald

A social networking system builds a quality controlled and desired population-representative pool of human raters to provide ratings on content items to improve a feed ranking model used for providing its users with more relevant content. The system identifies a pool of candidate human raters for providing ratings on a feed of content items. For each candidate human rater of the pool of candidate human raters, the system presents a feed of content items based on a feed ranking model, obtains ratings on the feed of content items, and determines a score representing the consistency of the obtained ratings, the representativeness of the pool of human raters, or the relevance of the content provided by the ranking model. The system uses the computed scores to modify the ranking model used to present content to its users for improving the relevance of the presented content.

Content relevance in a social networking system using population-representative human rater pool

An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.

Automatically tagging topics in posts during composition thereof

Content relevance in a social networking system using quality controlled human raters

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San Francisco, CA, United States of America

Max Christian Eulenstein