Dyna
BENEFITS
Optimize the reliability and accuracy of recommendations through adaptive ranking and weighting.
With a robust, reliable, explainable, and sustainable learning model
Use of preprocessed data, trained feature sets, and dynamic weight learning to reduce the impact of unreliable data.
Key points
Improve the quality and reliability of recommendations with continuous weight learning and adaptive ranking to better meet user needs with fewer computational resources.
Dynamic
The weight of sources is dynamically updated based on precision and recall.
Ranking calculations are iterative and adapted to improve robustness.
Sustainable
Based on a predefined duration, preprocessed data, and pre-trained weights to avoid excessive resource consumption.
Smart
Using adaptive machine learning to improve data source quality, reduce noise, and avoid corrupted data
