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:
Apr. 20, 2021

Filed:

Feb. 08, 2018
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Branislav Kveton, San Jose, CA (US);

Zheng Wen, Fremont, CA (US);

Prakhar Gupta, Bengaluru, IN;

Iftikhar Ahamath Burhanuddin, Bangalore, IN;

Harvineet Singh, Bengaluru, IN;

Gaurush Hiranandani, Urbana, IL (US);

Assignee:

ADOBE INC., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01); G06F 16/248 (2019.01); G06F 16/2457 (2019.01); G06Q 30/02 (2012.01);
U.S. Cl.
CPC ...
G06F 16/9535 (2019.01); G06F 16/248 (2019.01); G06F 16/24578 (2019.01); G06N 20/00 (2019.01); G06Q 30/02 (2013.01);
Abstract

A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.


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