Invention Grant
- Patent Title: Training and/or utilizing recurrent neural network model to determine subsequent source(s) for electronic resource interaction
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Application No.: US17072592Application Date: 2020-10-16
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Publication No.: US12099925B1Publication Date: 2024-09-24
- Inventor: Bryan Perozzi , Yingtao Tian
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: GOOGLE LLC
- Current Assignee: GOOGLE LLC
- Current Assignee Address: US CA Mountain View
- Agency: Gray Ice Higdon
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/044 ; G06N7/01

Abstract:
Systems, methods, and computer readable media related to training and/or utilizing a neural network model to determine, based on a sequence of sources that each have an electronic interaction with a given electronic resource, one or more subsequent source(s) for interaction with the given electronic resource. For example, source representations of those sources can be sequentially applied (in an order that conforms to the sequence) as input to a trained recurrent neural network model, and output generated over the trained recurrent neural network model based on the applied input. The generated output can indicate, for each of a plurality of additional sources, a probability that the additional source will subsequently (e.g., next) interact with the given electronic resource. Such probabilities indicated by the output can be utilized in performance of further electronic action(s) related to the given electronic resource.
Information query