Set-Based Recommendation Using Spatial Coverage for Personalized Diversity

The patent applies dynamics by transitioning from static content-based filtering to a dynamic set-based recommendation system that adapts to individual user characteristics. The system dynamically adjusts recommendations based on user-specific parameters (α, β, γ) that capture individual preferences, allowing the recommendation composition to change adaptively rather than remaining fixed based on content similarity alone.

The patent implements local quality by customizing recommendations for individual users through user-specific parameters. Each user receives tailored recommendations based on their unique characteristics (α for preference strength, β for exploration tendency, γ for diversity preference), rather than applying a uniform content-based approach to all users. This localized personalization resolves the contradiction by maintaining consistency within each user's preference profile while achieving diversity across different users.

The patent applies segmentation by dividing the homogeneous collaborative filtering approach into heterogeneous user-specific recommendation models. Instead of treating all users uniformly, the system segments users into individual profiles with unique parameters (α, β, γ), allowing the recommendation coverage to be maintained through group data while personalization is achieved through individual parameter customization.

The patent implements parameter changes by introducing user-specific parameters (α, β, γ) that modify the base collaborative filtering recommendations. These parameters are derived from individual user characteristics and adjust the recommendation output to reflect personal preferences, thereby maintaining coverage from group data while achieving individual personalization through parameter transformation.

The patent applies this principle by using lightweight parameter-based models (α, β, γ) instead of heavy machine learning models. These parameters can be quickly computed and updated with minimal computational resources, providing accurate recommendations without the high energy consumption associated with training and inference in complex machine learning systems.

The patent transforms the complex machine learning inference problem into a simpler parameter-based computation. By representing user preferences through concise parameters (α for preference strength, β for exploration tendency, γ for diversity preference), the system achieves recommendation accuracy with significantly reduced computational resource consumption compared to full machine learning approaches.