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:
Dec. 27, 2022

Filed:

Oct. 23, 2019
Applicant:

Amadeus S.a.s., Biot, FR;

Inventors:

Benoit Lardeux, Roquefort-les-Pins, FR;

David Renaudie, Valbonne, FR;

Rodrigo Alejandro Acuna Agost, Golfe Juan, FR;

Eoin Thomas, Antibes, FR;

Mourad Boudia, Valbonne, FR;

Papa Birame Sane, Vallauris, FR;

Assignee:

Amadeus S.A.S., Biot, FR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/00 (2012.01); G06Q 30/06 (2012.01); G06N 20/00 (2019.01); G06F 16/9535 (2019.01); G06Q 10/02 (2012.01); G06Q 30/02 (2012.01); H04L 67/306 (2022.01);
U.S. Cl.
CPC ...
G06Q 30/0631 (2013.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01); G06Q 10/02 (2013.01); G06Q 30/0242 (2013.01); G06Q 30/0253 (2013.01); G06Q 30/0254 (2013.01); G06Q 30/0633 (2013.01); H04L 67/306 (2013.01);
Abstract

Computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions. Second-level features are computed based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model is evaluated using the second-level features to generate a prediction of user behavior in relation to the list of proposed item recommendations. A personalized list of item recommendations is provided based upon the prediction generated by the second-level machine learning model.


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