Feature Combination Weighting for Fast CTR Effectiveness Assessment
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Solution Overview
Problem
Existing methods for assessing the effectiveness of feature combinations in content recommendation systems are time-consuming and inefficient, particularly when dealing with a large quantity of feature combinations, limiting the ability to quickly determine effective feature combinations for accurate CTR prediction.
Innovation Solution
A method that constructs feature value sets and weight value sets for feature combinations, allowing for the efficient assessment of effectiveness without time-consuming experiments or model training, by determining weight values based on feature values and using statistical methods to improve the efficiency of assessing a large quantity of feature combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental method or model training method is used to assess feature combination effectiveness, then assessment accuracy is improved, but assessment time and computational cost increase significantly
Solution Approach 1:
The patent pre-calculates and stores weight values for various feature combinations during an offline phase using historical data and statistical methods. During the online assessment phase, these pre-computed weight values are directly retrieved and used to evaluate feature combination effectiveness, eliminating the need for time-consuming real-time model training or experiments.
Solution Approach 2:
The patent creates a simplified representation of feature combination effectiveness using weight values that capture the essential relationships between features and CTR predictions. These weight values serve as a proxy model that replicates the assessment function without requiring full model training, enabling rapid evaluation of feature combination effectiveness.
2Reliability
If experimental method or model training method is used to assess feature combination effectiveness, then assessment reliability is improved, but computational resources and time consumption increase
Solution Approach 1:
The patent enables the system to self-assess feature combination effectiveness using pre-computed weight values and statistical formulas. The assessment process becomes autonomous and does not require external experimental validation or iterative model training, significantly improving assessment efficiency while maintaining reliability through statistically sound methods.
Solution Approach 2:
The patent transforms the assessment problem from a computational intensive model training problem to a statistical calculation problem by changing the parameters being evaluated. Instead of training models for each feature combination, the system uses pre-computed weight parameters that can be rapidly applied to assess effectiveness using simple statistical formulas.
Data Source
AI summary
This application discloses a feature effectiveness assessment method performed by an electronic device. The method including: constructing a feature combination set including a plurality of feature combinations, the feature combination being a combination of original features of to-be-recommended content; obtaining feature value sets respectively corresponding to the feature combinations, the feature value sets including feature values of combined features based on the corresponding feature combination; determining weight values corresponding to the combined features based on the feature values of the combined features in the corresponding feature value sets; constructing weight value sets corresponding to the feature combinations based on the weight values of the combined features; and respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations.