公开(公告)号：US11151661B2

公开(公告)日：2021-10-19

申请号：US15966583

申请日：2018-04-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yijie Wang ,  Souvik Ghosh ,  Timothy Paul Jurka ,  Shaunak Chatterjee ,  Wei Xue ,  Bonnie Barrilleaux

IPC: G06Q50/00 ,  G06F17/18 ,  G06N20/00 ,  G06F16/435 ,  G06F3/0482

Abstract: A plurality of potential feed objects and corresponding identifications of actors who performed a user interface action that caused a corresponding potential feed object to be generated are obtained. The plurality of potential feed objects and corresponding actor identifications are then fed into a machine learned feed object ranking model, with the machine learned feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects. The score is based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihood that the user's interaction will cause one or more downstream events by other users, and likelihood that a response from a viewer will cause the actor corresponding to the potential feed object to perform an additional user interface action to generate another potential feed object.

公开(公告)号：US20190333162A1

公开(公告)日：2019-10-31

申请号：US15966583

申请日：2018-04-30

Applicant: Microsoft Technology Licensing LLC

Inventor： Yijie Wang ,  Souvik Ghosh ,  Timothy Paul Jurka ,  Shaunak Chatterjee ,  Wei Xue ,  Bonnie Barrilleaux

IPC: G06Q50/00 ,  G06F17/30 ,  G06F3/0482 ,  G06F15/18 ,  G06F17/18

Abstract: A plurality of potential feed objects and corresponding identifications of actors who performed a user interface action that caused a corresponding potential feed object to be generated are obtained. The plurality of potential feed objects and corresponding actor identifications are then fed into a machine learned feed object ranking model, with the machine learned feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects. The score is based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihood that the user's interaction will cause one or more downstream events by other users, and likelihood that a response from a viewer will cause the actor corresponding to the potential feed object to perform an additional user interface action to generate another potential feed object.