Facebook, Inc.

Sagar Chordia

Harsh Doshi

Wei Wei

Tanmoy Chakraborty

Peng Chen

Zeyue Chen

Xianjie Chen

Qin Huang

Miao Li

A system trains models to generate embeddings that represent likelihoods associated with features. For example, an embedding may be generated for users and pages such that a user's embedding represents how likely a user is to comment on a given page. Initially, memory space for storing each embedding may be overprovisioned. The system monitors the embeddings for a feature as they are generated and recalculated over time. If the system detects that a particular index value is never updated for embeddings of that feature, then the system may remove that value from the feature embeddings. This allows the array lengths of embeddings to be customized to the particular features they represent, saving memory space. The system may further use related information to identify pooling functions that are most effective for particular features, to identify similarities between entities, and to provide insight into how the feature data influences neural network layers.

Optimization of feature embeddings for deep learning models

An online system provides a feed of content including organic content items and sponsored content items that are positioned relative to each other to maximize user interaction with the feed of content. To reduce latency of providing the feed of content to a user without impairing positioning of organic content items and sponsored content items relative to each other, the online system initially selects a subset of sponsored content items based on characteristics (e.g., bid amounts) of the sponsored content items. Subsequently, the online system applies one or more selection processes to organic content items and to sponsored content items of the subset that accounts for positioning of sponsored content items and organic content items relative to each other within the feed of content. Hence, the online system evaluates the subset of sponsored content items along with organic content items when ordering content within the feed.

Selecting sponsored content and organic content for presentation to an online system user while accounting for relative positioning of sponsored content and organic content

Growing community of inventors

Redwood City, CA, United States of America

Sagar Chordia