Facebook, Inc.

James Li

Shengbo Guo

Ying Zhang

Yixian Zhu

Sara Lee Su

Jiun-Ren Lin

Mark Warren McDuff

Christopher John Leggetter

Lu Wang

Michael Charles Bailey

Seyoung Park

Jiayi Wen

Yue Zhuo

Arun Babu

Kristin S Hendrix

Sijian Tang

Cheng Ju

Chenzhang He

Bram Wasti

Grace Louise Jackson

Systems, methods, and non-transitory computer readable media are configured to receive a uniform resource locator. A time and one or more features associated with the uniform resource locator can be provided to a first machine learning model. A prediction relating to a quantity of views the uniform resource locator achieves by the time can be received from the first machine learning model.

Systems and methods for scraping URLs based on viewport views

The disclosed computer-implemented method may include (1) maintaining a heterogeneous graph that represents (a) objects of a first type, (b) objects of a second type, and (c) relationships between the objects of the first type and objects of the second type, (2) using features of the objects of the first type to train a first embedding model to generate embeddings of the first type that (a) predict a label of objects of the first type and (b) predict, when combined with embeddings of the second type, graphical relationships in the graph, and (3) using features of each of the objects of the second type to train a second embedding model to generate the embeddings of the second type that predict, when combined with the embeddings of the first type, the graphical relationships in the graph. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for using heterogeneous graphs to derive object embeddings

The disclosed computer-implemented method may include (1) sampling links from an online system, (2) receiving, from a human labeler for each of the links, a label indicating whether the human labeler considers a landing page of the link to be a low-quality webpage, (3) generating a link graph from a crawl of the links, (4) using the link graph to derive a graph-based feature for each of the links, (5) using the label and the graph-based feature of each of the links to train a model configured to predict a likelihood that a link is to a low-quality webpage, (6) identifying content items that are candidates for a content feed of a user, (7) applying the model to the content items to determine a ranking, and (8) displaying the content items in the content feed based on the ranking. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for using link graphs to demote links to low-quality webpages

Systems, methods, and non-transitory computer readable media can generate a plurality of language clusters based on one or more of: language similarity between languages or social behavior similarity between languages. A representative language for a language cluster of the plurality of language clusters can be determined. For the language cluster of the plurality of language clusters, a machine learning model can be trained based on the representative language for the language cluster to classify content items in languages included in the language cluster.

Systems and methods for training machine learning models for language clusters

An online system receives posts that include links to various external pages and presents those posts to users of the online system. When the online system determines an opportunity to present a post to a particular viewing user of the online system, the online system determines a quality metric and an associated value score for the post. The quality metric is determined as a likelihood that the viewing user will view the external page for less than a threshold time period, and is used to adjust the associated value score. The online system compares the value score of the post to the value scores of other posts and selects one or more of the compared posts for presentation to the viewing user of the online system.

Selecting content with an external link for presentation based on user interaction with external content

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Pleasanton, CA, United States of America

James Li