The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 10, 2020

Filed:

Mar. 30, 2018
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

John Robert Jersin, San Francisco, CA (US);

Alexis Blevins Baird, San Francisco, CA (US);

Erik Eugene Buchanan, Mountain View, CA (US);

Benjamin John McCann, Mountain View, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/24 (2019.01); G06Q 10/10 (2012.01); G06Q 10/06 (2012.01); G06Q 50/00 (2012.01); G06F 16/9535 (2019.01); G06F 16/9032 (2019.01); G06F 16/28 (2019.01); G06N 20/00 (2019.01); G06F 16/2457 (2019.01); G06F 16/635 (2019.01); G06F 16/735 (2019.01); H04L 12/58 (2006.01); H04L 29/08 (2006.01); G06N 7/00 (2006.01); G06F 16/182 (2019.01);
U.S. Cl.
CPC ...
G06Q 10/1053 (2013.01); G06F 16/24578 (2019.01); G06F 16/285 (2019.01); G06F 16/635 (2019.01); G06F 16/735 (2019.01); G06F 16/9032 (2019.01); G06F 16/90324 (2019.01); G06F 16/9535 (2019.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G06Q 10/063112 (2013.01); G06Q 50/01 (2013.01); H04L 51/02 (2013.01); H04L 67/306 (2013.01); G06F 16/1834 (2019.01);
Abstract

Techniques for dynamically altering weights to re-weight attributes of an ordering model based on feedback in a streaming environment are described. In an embodiment, a system accesses, based on a candidate stream definition comprising a role including a title, one or more stream-related information sources, and extracts attributes from the sources. Additionally, the system inputs the attributes to a combined ordering model that is trained by a machine learning algorithm to output ordering scores for member profiles of an online system (e.g., hosting a social networking service). The combined ordering model includes weights assigned to the attributes. Moreover, the system orders, based on the ordering scores, the profiles. Furthermore, the system presents, on a display, of top-ordered profiles. Then, the system accesses feedback regarding the top-ordered profiles and dynamically trains the weights assigned to each of the attributes to alter the weights assigned to the attributes based on the feedback.


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