US 2026/0044577 A1Published application

Systems and methods for preference and similarity learning

AssigneeGeorgia Tech Research Corporation

InventorsGregory Canal, Christopher John Rozell, Stefano Fenu, Mark Davenport, Andrew Massimino

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Abstract

Preference and similarity learning systems and methods that improve efficiency for both searching datasets and embedding objects within the datasets. The systems and methods for preference embedding include identifying paired comparisons closest to a user's true preference point. The processes include removing obvious paired comparisons and/or ambiguous paired comparisons from subsequent queries. The systems and methods for similarity learning include providing larger rank orderings of tuples to increase the context of the information in a dataset. In each embodiment, the systems and methods can embed user responses in a Euclidean space such that distances between objects are indicative of user preference or similarity.

Claims10

Dependent claims

A method comprising:

receiving, at an input/output interface, a dataset comprising items;
adaptively selecting informative queries for active similarity learning;
and
based upon the adaptive selection:
similarity embedding the items in a similarity matrix;
and
subsequently repositioning one or more embedded items in the similarity matrix;
wherein the adaptive selection of informative queries comprises an iterative process of:
selecting, with a processor, a head item and a set of at least four body items from the items;
outputting for transmission, from the input/output interface, an informative query to a user device of an oracle, the informative query comprising the head item and the set of body items;
and
receiving, at the input/output interface and from the user device of the oracle, a ranking indicative of a ranking of each body item of the set of body items based on similarity to the head item;
wherein, based upon maximizing mutual information learned from each received ranking, the active similarity learning limits the number of informative queries needed for the similarity embedding and repositioning of the items in the similarity matrix;
wherein the similarity embedding and repositioning comprises embedding and repositioning, with the processor, the body items into/in the similarity matrix based on the rankings, wherein items in the similarity matrix are positioned/repositioned within the similarity matrix such that distances between any two items are indicative of similarity between the two items.

11 more elements

A method of embedding similar objects into a similarity matrix comprising:

receiving, at an input/output interface, a dataset comprising a plurality of objects;
selecting, with a processor, a first head object and a first set of body objects from the plurality of objects for a first ranking, the selection based on maximizing mutual information to be gained from the first ranking in the dataset, the first set of body objects comprising at least three objects;
outputting for transmission, from the input/output interface, a first query to a user device of an oracle, the first query comprising the first head object and the first set of body objects;
outputting for transmission, from the input/output interface, a plurality of queries to a plurality of oracles, the plurality of queries comprising a second head object and a second set of body objects;
receiving, at the input/output interface and from the user device of the oracle, a first ranking by an oracle indicative of a ranking of the first set of body objects based on similarity to the first head object;
receiving, at the input/output interface and from user devices of the plurality of oracles, a ranking set of the plurality of queries by each of the plurality of oracles indicative of a ranking of the second set of body objects based on similarity to the second head object;
embedding, with the processor, the first set of body objects into the similarity matrix based on the first ranking, wherein objects in the similarity matrix are positioned within the similarity matrix such that distances between any two objects are indicative of similarity between the two objects;
repositioning, with the processor, objects in the similarity matrix based on the oracle ranking;
and
embedding, with the processor, the second set of body objects into the similarity matrix;
wherein, based upon maximizing mutual information learned from the first ranking and the ranking set, active similarity learning of the method limits the number of queries needed for the similarity embedding and repositioning of the items in the similarity matrix.

8 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/365,620Filed 22 Oct 2025
Priority: 4 Feb 2019
Published: 12 Feb 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification5 codes

G06F 9/4451
Configuring for program initiating, e.g. using registry, configuration files: user profiles; Roaming
G06F 18/2185
Based on feedback of a supervisor: the supervisor being an automated module, e.g. intelligent oracle
G06F 9/451
Arrangements for executing specific programs: execution arrangements for user interfaces
G06F 17/11
Complex mathematical operations: for solving equations , e.g. nonlinear equations, general mathematical optimization problems

Family

Application62/800,666Us provisional application · filed 4 Feb 2019
Application62/800,686Us provisional application · filed 4 Feb 2019
Application17/428,598Division
Application19/365,620Division
US 12,499,178 B2Related patent
US 2022/0129709 A1Published application
US 2026/0044577 A1This patent

From the US filing history. Foreign family members aren’t listed.