Multi-Level Model Training for Missing Features
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Solution Overview
Problem
Existing personalized recommendation models, such as those based on logistic regression or RANKLR, face challenges in adapting to changing items with missing features, leading to inaccuracies and significant revenue loss, particularly in dynamic environments like news recommendation, where new items frequently appear with incomplete data.
Innovation Solution
A method for training a multi-level model in an information recommendation system by generating a feature group sequence, classifying the model based on this sequence, and training feature weight values to adapt to missing features, ensuring accurate recommendations by prioritizing feature stability and layered training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional personalized recommendation models (logistic regression or RANKLR) are used, then the model structure is simple and easy to implement, but the model accuracy deteriorates when items have missing features
Solution Approach 1:
The patent segments the feature set into multiple feature groups with different priorities, where each group contains features of the same type. This segmentation allows the model to systematically handle missing features by falling back to lower-priority groups, thereby maintaining recommendation accuracy without significantly complicating the model structure.
Solution Approach 2:
The patent introduces dynamic feature group sequencing that adapts based on feature availability. When higher-priority features are missing, the model dynamically adjusts to use features from lower-priority groups, making the recommendation system flexible and accurate in handling varying data completeness without requiring a completely complex model architecture.
2Measurement precision
If the model adapts to changing items with missing features, then the recommendation accuracy improves, but the model complexity increases
Solution Approach 1:
By dividing features into priority-based groups, the patent creates a structured adaptation mechanism that handles missing features systematically. This segmentation approach improves accuracy through adaptive feature selection while maintaining relatively simple model structure compared to other adaptive methods.
Solution Approach 2:
The patent changes the parameter organization by introducing feature group priorities and sequences as configurable parameters. This allows the model to adapt to missing features through parameter adjustments rather than structural changes, improving accuracy while controlling model complexity.
3Adaptability or versatility
If feature weight values are trained for each level of the multi-level model, then the model adapts better to missing features, but the training time and computational resources increase
Solution Approach 1:
The patent segments the training process into multiple levels corresponding to feature groups, where each level trains weight values for specific feature types. This segmented approach improves adaptability to missing features by enabling targeted training while reducing overall training time through hierarchical processing and selective feature evaluation.
Solution Approach 2:
The patent performs preliminary organization of features into priority-based groups before training. This preliminary action enables more efficient training by pre-establishing the feature evaluation order, allowing the model to quickly adapt to missing features during inference without requiring extensive retraining.
Data Source
AI summary
A method for training a model and an information recommendation system are provided. The method includes the following. Multiple types of features of the target model are obtained and a feature group sequence of the multiple types of features is generated, where the feature group sequence includes multiple feature groups and a sequence relation between the multiple feature groups and each feature group contains at least one type of features among the multiple types of features. The target model is classified into a multi-level model according to the feature group sequence. A trained target model is obtained by executing a preset training operation on the feature weight values corresponding to each level of the multi-level model.