AI Recommendation Multi-Indicator Aggregation Accuracy

The patent segments the recommendation process into multiple independent sorting models (e.g., click-through rate model, interest matching model, user engagement model) that operate in parallel. Each model focuses on a specific aspect of recommendation quality, allowing the system to achieve high overall accuracy without requiring a single overly complex model. This segmentation resolves the contradiction by distributing processing complexity across multiple specialized components rather than concentrating it in one complex system.

The patent introduces a multi-dimensional evaluation framework that assesses candidate information across multiple dimensions (click-through rate, interest matching, user engagement, diversity) simultaneously. This dimensional expansion allows the system to evaluate recommendations comprehensively without increasing the complexity of individual evaluation metrics. By adding evaluation dimensions rather than increasing metric complexity, the system achieves higher accuracy while maintaining manageable processing complexity.

The complex multi-level mapping and aggregation process is segmented into distinct modular components: feature extraction module, multi-level mapping module, multiple sorting model evaluation module, and aggregation module. Each module performs a specific function and can be independently optimized and maintained. This segmentation reduces system complexity by breaking down the monolithic complex process into manageable, specialized components that can be developed and deployed independently.

The patent designs universal sorting models and aggregation mechanisms that can handle multiple evaluation indicators (click-through rate, interest matching, engagement metrics) through a unified framework. Rather than creating separate specialized systems for each indicator, the universal framework processes all indicators through consistent multi-level mapping and aggregation logic, reducing overall system complexity while maintaining high recommendation accuracy across different metric types.

The system performs preliminary filtering and feature extraction before applying multiple sorting models. Candidate information is pre-processed to extract relevant features and eliminate obviously unsuitable options before entering the multi-model evaluation stage. This preliminary action reduces the number of candidates that require full multi-dimensional evaluation, thereby maintaining comprehensive evaluation capability while significantly reducing computational resource consumption and processing time.

The patent implements a staged evaluation approach where not all sorting models are applied to all candidate information equally. Instead, the system applies different levels of evaluation intensity based on initial screening results: high-potential candidates receive full multi-model evaluation, while lower-potential candidates receive simplified evaluation. This partial application of comprehensive evaluation maintains versatility for important cases while improving overall processing efficiency by avoiding excessive computation on unlikely candidates.