Facebook, Inc.

Prasoon Mishra

David Vickrey

Varun Kacholia

Jeffrey William Pasternack

Guy Dassa

Holly Marie Ormseth

Sahil Prakash Thaker

Khalid Bakry El-Arini

Max Christian Eulenstein

Yuanxuan Wang

Elad Gerson

Jianfu Chen

Sanjeet Uday Hajarnis

Gaurav Shankar

Justin MacLean Coughlin

Austen Norment McDonald

Kritarth Anand

Polina Kuznetsova

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

Systems, methods, and non-transitory computer readable media configured to detect access by a user to an original content item relating to a story. At least one of a comments based technique, a token based technique, and a tag based technique is performed on content items. Constraints are applied to identify at least one follow up content item from the content items relating to a development of the story.

Systems and methods for recommending follow up content

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Mountain View, CA, United States of America

Prasoon Mishra