Twitter, Inc.

Sandeep Pandey

Michael Jahr

Utkarsh Srivastava

Chinmoy Dutta

Yue Lu

Vibhor Rastogi

Junjun Li

Wanchen Lu

Ashish Goel

Lei Zhang

Shariq Rizvi

Ameet Ranadive

Parag Agrawal

Reid Andersen

Jeremy Ginsberg

Santosh Kumar Kancha

Yimin Wu

Abhishek Shrivastava

Xiaochuan Qin

Ke Zhou

Cheng Li

James Neufeld

Milind Mahajan

Sridhar Vishwanathan Iyer

Pengjun Pei

Yong Wang

Ziqi Huang

Xiaojiang Guo

Van Lam

Lanbo Zhang

Bingjun Sun

Qi Li

Dong Wang

Zhiyong Xie

Gary Lam

Shuanghong Yang

A real-time messaging platform allows advertiser accounts to pay to insert candidate messages into the message streams requested by account holders. To accommodate multiple advertisers, the messaging platform controls an auction process that determines which candidate messages are selected for inclusion in a requested account holder's message stream. Selection is based on a bid for the candidate message, the message stream that is requested, and a variety of other factors that vary depending upon the implementation. The process for selection of candidate messages generally includes the following steps, though any given step may be omitted or combined into another step in a different implementation: targeting, filtering, prediction, ranking, and selection.

Exploration in a real time messaging platform

In some examples, a computing device includes at least one processor and at least one module, operable by the at least one processor to receive, from a client device of a user, a request for one or more advertisements to display at the client device with a set of messages. The set of messages is associated with the user in a social network messaging service. The at least one module may be further operable to determine a probability that the user will select a candidate advertisement using a machine learning model based on point-wise learning and pair-wise learning. The at least one module may be further operable to determine, based on the probability that the user will select the candidate advertisement, a candidate score for the candidate advertisement, determine that the candidate score satisfies a threshold, and send, for display at the client device, the candidate advertisement.

Click-through prediction for targeted content

An example system includes prediction workers, training workers, and a parameter server. The prediction workers store a local copy of a machine-learned model and run the mode exclusively in serving mode. The training workers store a local copy of a machine-learned model and a local snapshot and run the local copy exclusively in training mode and compare the local model or state to the snapshot after training to send delta updates to the parameter server after training. The parameter server aggregates received delta updates into a master copy of the model, sends the aggregated updates back to training workers and provides two types of updates; a real-time update based on a comparison of the master model with a local snapshot, and a full update. The real-time update occurs at least an order of magnitude more frequently than the full update and includes a subset of the weights in the model.

Efficient online training for machine learning

A method for conversion attribution. The method includes obtaining a first identifier associated with a first device, obtaining a second identifier associated with a second device, bridging the first identifier and the second identifier based on a determination, made by a probabilistic classifier, that the first identifier and the second identifier are associated with a common user, and attributing, using the bridge, a conversion on a website accessed using the second device.

Method and system for identifying users across mobile and desktop devices

Unidirectional lookalike campaigns in a messaging platform

Growing community of inventors

San Francisco, CA, United States of America

Sandeep Pandey