公开(公告)号：US20190108209A1

公开(公告)日：2019-04-11

申请号：US15825657

申请日：2017-11-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Karan Ashok Ahuja ,  Befekadu Ayenew Ejigou ,  Ningfeng Liang ,  Lokesh P. Bajaj ,  Wei Wang ,  Paul Fletcher ,  Wei Lu ,  Shaunak Chatterjee ,  Souvik Ghosh ,  Yang Li ,  Wei Deng ,  Qiang Wu

IPC: G06F17/24 ,  G06F15/18 ,  G06Q50/00 ,  H04L29/08

Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

公开(公告)号：US20240378424A1

公开(公告)日：2024-11-14

申请号：US18214905

申请日：2023-06-27

Applicant: Microsoft Technology Licensing, LLC

Inventor： Praveen Kumar Bodigutla ,  Suman Sundaresh ,  Souvik Ghosh ,  Saurabh Gupta ,  Sai Krishna Bollam ,  Arya Ghatak Choudhury ,  Weiheng Qian ,  Jiarui Wang

IPC: G06N3/0455 ,  G06N3/08

Abstract: Embodiments of the disclosed technologies include configuring a first machine learning model to generate and output suggested message content based on first correlations between message content and message acceptance data, where the first machine learning model includes a first encoder-decoder model architecture, configuring a second machine learning model to generate and output message evaluation data based on second correlations between the message content and the message acceptance data, where the second machine learning model includes a second encoder-decoder model architecture, coupling an output of the first machine learning model to an input of the second machine learning model, and coupling an output of the second machine learning model to an input of the first machine learning model.

公开(公告)号：US20190188323A1

公开(公告)日：2019-06-20

申请号：US15844032

申请日：2017-12-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Souvik Ghosh ,  Timothy Paul Jurka ,  Sergei Tolmanov ,  Yijie Wang

IPC: G06F17/30 ,  G06F15/18 ,  G06Q50/00 ,  H04L29/08

CPC classification number: G06F16/9535 ,  G06N20/00 ,  G06Q50/01 ,  H04L67/306

Abstract: In an example, a plurality of potential feed objects are obtained. An identification of a user performing a navigation command in a user interface is also obtained, the navigation command causing a feed to be displayed or updated. The identification of the user and the plurality of potential feed objects are fed to a machine learned feed object ranking model, the 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 being based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihoods that the user's interaction will cause one or more downstream events by other users, and a value of the one or more downstream events to a social networking service. The plurality of feed objects are ranked by their scores.