Facebook, Inc.

Mircea Grecu

Tianshi Gao

Shyamsundar Rajaram

Stuart Michael Bowers

Vibhor Rastogi

Puneet Sharma

When an online system receives a request to present content items to a user, a content selection system included in the online system selects content items for presentation to the user. A feedback control mechanism communicates with each computing device of the content selection system to determine the latency period and the CPU utilization of each computing device. The feedback control mechanism also determines a target latency period and a target CPU utilization in which content items are selected. By comparing the latency period of each computing device to the target latency period and the CPU utilization to the target CPU utilization, an amount of information to be evaluated by each computing device is determined based on the comparisons.

Latency and CPU utilization control for content item selection

Some embodiments include a method of generating a compatibility score for a grouping of objects based on correlations between attributes of the objects. An example grouping is a pair of user and ad. The method may be implemented using a multi-threaded pipeline architecture that utilizes a learning model to compute the compatibility score. The learning model determines correlations between a first object's attributes (e.g., user's liked pages, user demographics, user's apps installed, pixels visited, etc.) and a second object's attributes (e.g., expressed or implied). Example expressed attributes can be targeting keywords; example implied attributes can be object IDs associated with the ad.

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Menlo Park, CA, United States of America

Mircea Grecu