Media Recommendation Weighting Using Differential Evolution

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Solution Overview

Problem

Media distribution systems struggle to select an optimal combination of content recommendation algorithms efficiently, leading to inefficient and time-consuming manual selection by human curators.

Innovation Solution

Utilizing differential evolution techniques to iteratively adjust weight combinations of recommendation algorithms based on empirical data to achieve an optimal or near-optimal selection of media content recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual selection by human curators is used to select recommendation algorithms, then adaptability to user preferences is maintained, but productivity and time efficiency deteriorate

Engineering Contradiction:

Improveadaptability to user preferencesVSAvoidproductivity

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system performs self-service by automatically selecting and weighting recommendation algorithms without human intervention. The differential evolution algorithm enables the system to autonomously optimize algorithm combinations based on performance metrics, eliminating the need for manual curator selection while maintaining adaptability through automated performance-based adjustments.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical human decision-making process with an automated computational system. The differential evolution algorithm substitutes human curators' manual algorithm selection with a mathematical optimization process that automatically determines optimal algorithm weightings based on empirical performance data.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If multiple recommendation algorithms are used to improve recommendation quality, then measurement precision of user preferences improves, but device complexity increases

Engineering Contradiction:

Improvemeasurement precisionVSAvoiddevice complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system changes parameters by dynamically adjusting the weightings of multiple recommendation algorithms rather than using a fixed combination. The differential evolution algorithm optimizes these weightings as adjustable parameters, allowing the system to adapt the contribution of each algorithm based on performance metrics while managing complexity through parameter optimization.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces dynamics by making algorithm weightings changeable and adaptive rather than static. The system continuously adjusts algorithm contributions based on performance feedback, transforming a complex multi-algorithm system into a dynamically optimized configuration that adapts to changing user preferences and content characteristics.

Inventive Principle:

Principle #15Dynamics

3Productivity

If iterative optimization is implemented to optimize algorithm combinations, then productivity improves, but loss of time increases

Engineering Contradiction:

ImproveproductivityVSAvoidloss of time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-computing and caching algorithm weightings through iterative optimization before they are needed for actual recommendations. The differential evolution algorithm performs exhaustive search and optimization in advance, storing the results for rapid deployment, thus reducing real-time computational overhead and time loss during actual recommendation generation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements periodic action by running iterative optimization at scheduled intervals rather than continuously. The system periodically re-optimizes algorithm weightings based on accumulated performance data, balancing the need for optimized recommendations with the computational time required for optimization, thus managing the trade-off between productivity improvement and time consumption.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS20260111758A1Systems and methods for providing media content recommendations

Publication Date: 2026.04.23 ADEIA GUIDES INC

AI summary

Systems and associated methods are described for providing content recommendations. The system accesses a plurality of recommendation algorithms and assigns a plurality of weight values to each prediction algorithm. Then, the system generates a set of candidate weight combinations, such that each candidate combination includes a weight value assigned to each prediction algorithm. Then requests for content items are received over a predetermined period of time. For each combination, the system generates a set of recommended content items and an evaluation metric that is based on matches with requests. Afterwards, the system replaces a candidate combination that resulted in a generation of a lowest evaluation metric. The aforementioned steps are repeated until the evaluation metrics stop improving. Then display identifiers are displayed for a set of recommended content items generated for a candidate combination with the highest evaluation metric.