Microsoft Technology Licensing, LLC

Bing Wang

Yan Liu

Yongzheng Zhang

Peter Bernard Key

Li Yang

Laurent Massoulie

Kaiyu Yang

Ajita Thomas

Alexander Shoykhet

Ying Xi

Neil Scott Davenport

A spammer profile detector uses multi-stage machine learning approach, where a content-based machine learning model, a connection graph machine learning model, and a behavior-based machine learning model are used sequentially, each model generating a score indicating the likelihood that a profile is a spammer profile. The content-based machine learning model examines and evaluates information stored in a member profile. The connection graph machine learning model examines and evaluates a member's connections. The behavior-based machine learning model examines and evaluates activities of a member represented by a member profile. The score produced by the spammer profile detector can be used to determine whether the profile should be flagged as a spammer profile, whether the profile should be omitted when determining a count of the total number of active member profiles within the system, whether the profile should be restricted or removed from the system, etc.

Identifying spammer profiles

The technical problem of identifying relevant social proof information with respect to a premium service for a given member profile in an online connection network system is addressed by, first, capturing the associated member's intent based on the member's activity on the web site provided by the online connection network system. The determined intent is used as input into a relevance machine learning model that is executed to identify the member's connection who is a subscriber to the premium service and who has been identified as the most convincing resonated connection of the member with respect to subscribing to the premium service.

Machine learning based system for identifying resonated connections in online connection networks

Techniques for providing a secure and verifiable data access logging system are disclosed herein. In some embodiments, a computer system receives an indication of a data request from a client device that is requesting data of one or more users from a data server, stores a request log entry corresponding to the data request in a log file, generates a request token based on the received indication of the data request, transmits the generated request token to the client device, receives a fetch event from the data server that requests a request digest corresponding to the request token and configured to indicate that the request log entry corresponding to the data request is stored in the log file, stores a response log entry corresponding to the received fetch event in the log file, and transmits the request digest to the data server based on the received fetch event.

Secure and verifiable data access logging system

In an application-level background transport service, a receiver node infers the available network capacity between itself and a sender node over a control interval. Based on the inferred available network capacity, the receiver node adjusts its receive window size accordingly in order to conservatively optimize the bandwidth used by a background transfer without degrading performance of other foreground transfers on the network. The adjusted receive window size is communicated to the sender node, which is likely to adjust its send window size based on the adjusted receive window size.

Background transport service

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Mountain View, CA, United States of America

Bing Wang