Maximum-Flow Media Recommendation Calibration for Minority Interests

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Solution Overview

Problem

Existing recommendation systems for digital media consumption are inefficient and uncalibrated, failing to account for users' minority interests due to NP-hard formulations, leading to suboptimal recommendations.

Innovation Solution

A method using network graphs to represent user interests and media item relevance, optimizing for maximum flow/minimum cost to provide calibrated recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a system optimizes for relevance alone by independently maximizing the likelihood that each recommendation is of interest to the user, then the relevance of recommendations is improved, but the calibration to represent users' full interest distribution deteriorates

Engineering Contradiction:

Improverelevance of recommendationsVSAvoidcalibration to user interest distribution

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent merges the relevance optimization objective with the calibration objective into a unified framework. It combines the relevance scoring mechanism with the distribution calibration mechanism by formulating both as constraints in a single optimization problem, thereby achieving both high relevance and proper calibration simultaneously without the trade-off present in separate optimization approaches

Inventive Principle:

Principle #5Merging (Combining)

2Measurement precision

If existing NP-hard formulations of the calibration problem are used, then the calibration accuracy is improved, but the computation time deteriorates

Engineering Contradiction:

Improvecalibration accuracyVSAvoidcomputation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent changes the mathematical formulation parameters by transforming the NP-hard calibration problem into a linear programming problem. This parameter change involves reformulating the optimization objective and constraints in a way that enables polynomial-time solution methods, thereby maintaining calibration accuracy while significantly reducing computation time requirements

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent substitutes the complex iterative optimization mechanisms with a linear programming framework. This substitution replaces the need for complex computational algorithms with a more efficient linear programming approach that can be solved using standard polynomial-time methods, thereby reducing computational complexity while maintaining calibration accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12361052B2Systems and methods for calibrating recommendations using a maximum flow approach

Publication Date: 2025.07.15 SPOTIFY

AI summary

An electronic system stores metadata for a plurality of media items, including, for each media item of the plurality of media items, at least one categorical identifier from a set of categorical identifiers. For a user of the media-providing service, the electronic system (i) determines a distribution of interests of the user with respect to the set of categorical identifiers; (ii) generates a network graph configured to represent a calibrated media item selection task, wherein the network graph represents respective relevance scores for each respective media item of the plurality of media items and the distribution of interests of the user with respect to the categorical identifiers; (iii) selects a set of media items from the plurality of media items to recommend to the user by solving for a maximum flow of the network graph; and (iv) provides the set of media items as recommendations to the user.