Yahoo Ad Tech LLC

Verizon Media Inc.

Niru Appikatala

Miao Chen

Joshua Walters

Sudhir Chauhan

Michael Natkovich

Balachandar Sankar

Chandrashekhar Shaw

SansWord Huang

Eyan Goldman

Shweta Tripathi

The present teaching generally relates to identifying data bucket overlap with online experiments. In a non-limiting embodiment, first data representing a first set of identifiers associated with a first data bucket of a first online experiment may be obtained. Second data representing a second set of identifiers associated with a second data bucket of the first online experiment may be obtained. Based on the first data and the second data, a first number of identifiers that are associated with the first data bucket and the second data bucket may be determined. In response to determining that the first number exceeds a threshold, a data flag indicating that results associated with the first online experiment are inconsistent may be generated.

Method and system for detecting data bucket inconsistencies for A/B experimentation

In an example, an article may be analyzed to identify entity terms. Entity term relevance scores associated with the entity terms may be determined based upon the article and the entity terms. One or more first entity terms may be selected based upon the entity term relevance scores. One or more sets of reference position information associated with the one or more first entity terms may be determined. A first set of reference position information is based upon one or more positions, in the article, of one or more references to a first entity term. One or more second entity terms of the one or more first entity terms may be selected based upon the one or more sets of reference position information. A set of one or more salient entity tags associated with the article may be generated based upon the one or more second entity terms.

Determining salient entities and generating salient entity tags based upon articles

The present teaching generally relates to detecting data bucket discrepancies associated with online experiments. In a non-limiting embodiment, a monitoring layer may be generated within an online experimentation platform that includes at least a first layer, and where a first online experiment is associated with the first layer, the monitoring layer includes a monitoring layer data bucket, and the first layer includes at least a first data bucket. First data representing user activity associated with a first plurality of identifiers may be obtained, the user activity being associated with the first layer. Second data representing at least one user engagement parameter may be generated, and a first discrepancy between the first and second data may be determined. The first discrepancy indicating a first amount of identifiers that include a first metadata tag associated with the first layer and lack a second metadata tag associated with the monitoring layer.

Method and system for detecting gaps in data buckets for A/B experimentation

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Niru Appikatala