Evolutionary Content Category Selection for Carousel Duplication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Media distribution systems face challenges in providing effective media content recommendations as a stack of carousels, where popular content may be overlooked and excessive duplication leads to sub-optimal user interface space utilization and negative user experience.
Innovation Solution
The system employs an evolutionary approach to select a subset of content categories with an optimal number of categories, using an evolutionary technique to maximize reward scores by adjusting and combining subsets based on popularity and duplication rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If content categories are selected manually by a human editor, then the selection process is simple and controllable, but popular content may fail to appear in the carousels and the selection is sub-optimal
Solution Approach 1:
The system performs self-service by automatically selecting content categories and optimizing carousel configurations without human intervention. The evolutionary algorithm autonomously evaluates multiple category subsets, calculates reward scores based on popularity and duplication metrics, and selects the optimal configuration, eliminating the need for manual editor selection while achieving superior results.
Solution Approach 2:
The patent replaces the mechanical system of manual human editing with an automated computational system. Instead of human editors manually selecting categories, an evolutionary algorithm with automated scoring and selection mechanisms evaluates and optimizes category subsets, substituting human cognitive processes with computational optimization.
2Quantity of substance
If the number of categories is increased to show more content, then more diverse content is available, but the same content excessively appears in multiple carousels taking up limited user interface space
Solution Approach 1:
The system implements feedback by calculating reward scores that incorporate duplication penalties. When content appears excessively across multiple carousels, the reward score decreases, providing negative feedback that guides the evolutionary algorithm to adjust category selections. This feedback mechanism balances category diversity with space efficiency by penalizing configurations with excessive duplication.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the number of categories and their compositions based on reward score evaluations. The evolutionary algorithm modifies category subset parameters across generations, optimizing the balance between quantity of categories and duplication levels, thereby maximizing interface space utilization while maintaining content diversity.
3Productivity
If evolutionary optimization is applied to maximize reward scores, then popular content is better presented with reduced duplication, but the computational complexity and processing time increase
Solution Approach 1:
The system applies partial action by evaluating a controlled number of category subsets rather than exhaustively searching all possible combinations. The evolutionary algorithm performs a limited number of generations with a specific population size, achieving sufficient optimization without exhaustive computation. This partial action approach balances presentation quality with acceptable processing time.
Data Source
AI summary
Systems and associated methods are described for providing content recommendations. The system selects a first plurality of subsets of content categories, each subset of content categories comprising a first number of content categories. The subsets are assigned reward scores based on content popularity and duplication. The subset are then iteratively modified to increase the rewards scores. If the reward scores are still low, the process is repeated by selecting a second plurality of subsets of content categories, each subset of content categories comprising a second number of content categories, different from first number.