Dstillery, Inc.

Brian Dalessandro

Rodney Alan Hook

Ori M Stitelman

Andrew Pancer

Blake Aber

Claudia Reisz

Yuva Manhendran

Claudia Riesz

Some embodiments described herein relate to a method that includes surveying and/or passively monitoring content consumption by mobile communication devices. Each signal received from a mobile communication device can be associated with a common location. For example, an out-of-home device positioned at the location can be operable to receive requests from mobile communication devices that represent requests to access network locations. An audience profile for that common location can be defined based on a network location that is identified as being statistically overrepresented. Content targeted to the audience profile at the common location can then be provided.

Methods and apparatus for defining an audience for a particular location by surveying nearby receivers and/or passively monitoring content consumption

A non-transitory processor-readable medium is provided that stores code representing instructions to be executed by a processor to receive data associated with access by a first plurality of entities to a first website location and to receive data associated with access by a second plurality of entities to a second website location. The processor is also caused to define a co-visitation factor for each of the first website location and the second website location based on the received data. The processor is also caused to, if the co-visitation factor of the first website location and/or the co-visitation factor of the second website location is over a predefined threshold, select the first website location and/or the second website location as target website locations. The processor is caused to send a signal to set a flag associated with each target website location indicating the target website location as a suspicious website location.

Methods, systems and media for detecting non-intended traffic using co-visitation information

A complete end-to-end modeling system is provided that includes data sampling, feature engineering, action labeling, and model learning or learning from models built based on collected data. The end-to-end modeling process is performed via an automatic mechanism with minimal or reduced human intervention. A processor-readable medium is disclosed, storing processor-executable instructions to instantiate an automated data sampling and prediction structure training component, the automated data sampling and prediction structure training component being configured to automatically collect user event data samples, and use the collected user event data samples to train multiple prediction structures in parallel.

Methods and system for providing simultaneous multi-task ensemble learning

A non-transitory processor-readable medium stores code representing instructions to be executed by a processor to receive data associated with access by a first plurality of entities to a first website location and to receive data associated with access by a second plurality of entities to a second website location. The processor is also caused to define a co-visitation factor for each of the first website location and the second website location based on the received data. The processor is also caused to, if the co-visitation factor of the first website location and/or the co-visitation factor of the second website location is over a predefined threshold, select the first website location and/or the second website location as target website locations. The processor is caused to send a signal to set a flag associated with each target website location indicating the target website location as a suspicious website location.

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Brooklyn, NY, United States of America

Brian Dalessandro