IP Library › Granted Patent US 12,499,378
Granted Patent B2
US 12,499,378 · App. 17/530,883 · Granted Dec 16, 2025

Fairness utility trade-off in ranking as a geometric projection problem

Inventors: Till Kletti (La Tronche, FR); Jean-Michel Renders (Quaix en Chartreuse, FR)
Assignee: NAVER CORPORATION
G06N7/01G06Q30/0625
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Quick Facts
Patent No.
US 12,499,378
App. No.
17/530,883
Granted
Dec 16, 2025
Kind
B2
Abstract

A ranking system includes: an exposure module configured to, in response to receiving an input from a computing device via a network, determine, based on an anisotropic intensity value and using a Dynamic Bayesian Network (DBM) exposure model, an exposure value for ranking items for output via the computing device, where the anisotropic intensity value corresponds to a tradeoff between utility to users and fairness to item producers; a ranking module configured to generate a ranking of the items based on the exposure value; and a response module configured to transmit a response including the ranking of the items to the computing device via the network.

Claims (49)

1 . A ranking system, comprising:

an exposure module configured to, in response to receiving an input from a computing device via a network, determine, based on an anisotropic intensity value and using a Dynamic Bayesian Network (DBN) exposure model, an exposure value for ranking items for output via the computing device,

wherein the anisotropic intensity value corresponds to a tradeoff between utility to users and fairness to item producers,

wherein the exposure module is configured to determine a shape based on the anisotropic intensity value and determine the exposure value using the shape;

a ranking module configured to generate a ranking of the items based on the exposure value; and

a response module configured to transmit a response including the ranking of the items to the computing device via the network.

2 . The ranking system of claim 1 further comprising an administrator module configured to set the anisotropic intensity value based on user input.

3 . The ranking system of claim 1 wherein the ranking module is configured to generate the ranking of the items further based on relevance values for the items, respectively.

4 . The ranking system of claim 3 wherein the relevance values are unknown.

5 . The ranking system of claim 3 wherein the relevance values are known and stored in memory.

6 . The ranking system of claim 1 wherein the ranking module is configured to generate the ranking further based on a frequency of the input.

7 . A system, comprising:

the ranking system of claim 1 ; and

the computing device, wherein the computing device is configured to at least one of:

display the items on a display in order according to the ranking; and

output the items via a speaker in order according to the ranking.

8 . The ranking system of claim 1 wherein the shape is an ellipse and the exposure module is configured to determine the exposure value using the ellipse.

9 . The ranking system of claim 8 wherein the exposure module is configured to determine characteristics of the ellipse based on the anisotropic intensity value.

10 . The ranking system of claim 8 wherein the exposure module is configured to determine the exposure value further based on a target exposure vector.

11 . The ranking system of claim 10 wherein the target exposure vector includes exposures for the items, respectively, when averaged over all probability ranking principle (PRP) rankings.

12 . The ranking system of claim 10 wherein the fairness to item producers is at least one of meritocratic fairness and demographic fairness.

13 . A ranking method, comprising:

in response to receiving an input from a computing device via a network, determining, based on an anisotropic intensity value and using a Dynamic Bayesian Network (DBN) exposure model, an exposure value for ranking items for output via the computing device,

wherein the anisotropic intensity value corresponds to a tradeoff between utility to users and fairness to item producers;

determining a shape based on the anisotropic intensity value,

wherein determining the exposure value includes determining the exposure value using the shape;

generating a ranking of the items based on the exposure value; and

transmitting a response including the ranking of the items to the computing device via the network.

14 . The ranking method of claim 13 further comprising setting the anisotropic intensity value based on user input.

15 . The ranking method of claim 13 wherein generating the ranking includes generating the ranking of the items further based on relevance values for the items, respectively.

16 . The ranking method of claim 15 wherein the relevance values are unknown.

17 . The ranking method of claim 15 wherein the relevance values are known and stored in memory.

18 . The ranking method of claim 13 wherein generating the ranking includes generating the ranking further based on a frequency of the input.

19 . The ranking method of claim 13 further comprising, by the computing device, at least one of:

display the items on a display in order according to the ranking; and

output the items via a speaker in order according to the ranking.

20 . The ranking method of claim 13 wherein:

the shape is an ellipse; and

determining the exposure value includes determining the exposure value using the ellipse.

21 . The ranking method of claim 20 further comprising determining characteristics of the ellipse based on the anisotropic intensity value.

22 . The ranking method of claim 20 further comprising determining the exposure value further based on a target exposure vector.

23 . The ranking method of claim 22 wherein the target exposure vector includes exposures for the items, respectively, when averaged over all probability ranking principle (PRP) rankings.

24 . The ranking method of claim 22 wherein the fairness to item producers is at least one of meritocratic fairness and demographic fairness.

25 . A ranking system, comprising:

a means for, in response to receiving an input from a computing device via a network, determining, based on an anisotropic intensity value and using a Dynamic Bayesian Network (DBN) exposure model, an exposure value for ranking items for output via the computing device,

wherein the anisotropic intensity value corresponds to a tradeoff between utility to users and fairness to item producers,

wherein means for determining the exposure value is for determining a shape based on the anisotropic intensity value and determining the exposure value using the shape;

a means for generating a ranking of the items based on the exposure value; and

a means for transmitting a response including the ranking of the items to the computing device via the network.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 19, 2021
From: KLETTI, TILL; RENDERS, JEAN-MICHEL
To: NAVER CORPORATION
Reel/Frame 058165/0204 →
Continuity (1)
Related Publication 20230162065A1 · May 25, 2023
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