Learning-to-Rank Substitution System for Contextual Relevance
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Solution Overview
Problem
Current substitution systems are unable to identify contextually relevant substitute elements and struggle with 'cold start' problems where there is little or no historical information available for target elements.
Innovation Solution
A system that uses a trained candidate selection model and a trained ranking model to generate and rank candidate substitution elements based on an input set including an anchor element, a feature set, and a catalog of elements, with the ability to update the models using feedback data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single approach is used for selection and ranking of substitute elements, then the system is simple to implement, but it cannot identify contextually relevant substitute elements or handle cold start problems
Solution Approach 1:
The substitution system is divided into two distinct models: a candidate selection model that identifies potential substitutes, and a ranking model that ranks them by contextual relevance. This segmentation allows each model to specialize in its function, enabling the system to handle contextual relevance and cold start problems while maintaining manageable complexity through modular design.
2Reliability
If historical information is used for substitution, then the system can learn from past data, but it fails when little or no historical information is available
Solution Approach 1:
The candidate selection model is pre-trained on historical substitution data to learn general patterns of suitable replacements. This preliminary training enables the model to make informed predictions even when specific historical data for a particular anchor element is unavailable, effectively solving the cold start problem while maintaining high substitution accuracy through the learned patterns.
3Measurement precision
If multiple models are used for candidate selection and ranking, then contextual relevance is improved, but computational resources and processing time increase
Solution Approach 1:
The system segments the substitution task into two sequential stages: candidate selection and ranking. The candidate selection model quickly filters potential substitutes, and the ranking model then focuses computational resources on ranking only these selected candidates. This segmentation improves ranking precision while controlling processing time by avoiding the need to rank all catalog elements.
Solution Approach 2:
Instead of ranking all catalog elements, the system applies the computationally intensive ranking model only to a subset of candidates selected by the first model. This partial application of the ranking process maintains high precision for the most relevant substitutes while significantly reducing overall computational resource consumption and processing time.
Data Source
AI summary
Systems and methods for identifying substitute elements for computer-implemented processes are disclosed. A substitution request identifying an anchor element is received. A set of candidate substitution elements is generated by a trained candidate selection model configured to receive the anchor element, a feature set, and a set of catalog elements. The set of candidate substitution elements is ranked by a trained ranking model configured to receive the anchor element, the feature set, and the set of candidate substitution elements. At least one substitution element is selected from the set of candidate substitution elements and feedback data representative of the suitability of the selected at least one substitution element with respect to the anchor element is received. At least one of the trained candidate selection model or the trained ranking model is updated by applying an iterative training process incorporating at least a portion of the feedback data.