Yahoo Assets LLC

Yahoo Ad Tech LLC

Chander Jayaraman Iyer

Lakshmi Narayan Bhamidipati

Srinath Ravindran

Ivan Stojkovic

Shubham Agrawal

Eric Ye

Djordje Gligorijevic

Narayan Bhamidipati

Gaurav Batra

Richard Hin-Fai Tang

An Jiang

Martin Pavlovski

Jelena Gligorijevic

Shabhareesh Komirishetty

Xiao Bai

Ritest Agrawal

One or more computing devices, systems, and/or methods are provided. In an example, an internet resource identification item associated with one or more internet resources may be identified. User activity information associated with a plurality of events may be analyzed to determine a plurality of sets of text associated with the internet resource identification item, wherein each set of text of the plurality of sets of text is associated with an event, of the plurality of events, associated with an internet resource of the one or more internet resources. A plurality of term representations may be determined based upon the plurality of sets of text. A user intention-based representation associated with the internet resource identification item may be generated based upon the plurality of term representations. A content item may be selected for presentation via a client device based upon the user intention-based representation.

Determination of user intention-based representations of internet resource identification items and selection of content items

The techniques described herein relate to constructing and using seed audiences. In an embodiment, a method includes loading, by a processing device, a user event sequence, the user event sequence including a plurality of user events and a plurality of corresponding conversions; generating, by the processing device, a plurality of conversion neighborhoods based on the user event sequence, a given conversion neighborhood in the plurality of conversion neighborhood including at least one conversion rule and a set of user events from the plurality of user events; annotating, by the processing device, each conversion neighborhood in the plurality of conversion neighborhoods with categorical labels; and generating, by the processing device, seed audiences for each conversion neighborhood, a given seed audience including a ranked list of user events for each conversion rule associated with the conversion neighborhood.

Cross-entity categorization of heterogenous data

One or more computing devices, systems, and/or methods for generating time-preserving embeddings are provided. User trails of user activities performed by users are generated. Frequencies at which the activities were performed are identified. Indices are assigned to a set of activities identified from the activities as having frequencies above a threshold. Activity descriptions of the set of activities are mapped to the indices to generate a vocabulary. A model is trained using the user trails, timestamps of the activities, and the vocabulary to learn a set of time-preserving embeddings.

Time-preserving embeddings

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Santa Clara, CA, United States of America

Chander Jayaraman Iyer