Multi-tiered Information Retrieval Training for Ranking Accuracy
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Solution Overview
Problem
Conventional learning to rank algorithms in information retrieval systems face limitations in accurately distinguishing results with the same relevance label and inadequately gauging user intent, leading to suboptimal ranking model effectiveness.
Innovation Solution
A multi-tiered information retrieval training methodology that utilizes multiple ranking quality metrics, including user click data, to determine vectors for results with the same relevance label, combining these vectors to enhance ranking model accuracy and reflect user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional learning to rank algorithms use single relevance labels for training, then training simplicity is maintained, but the ability to distinguish results with the same relevance label deteriorates
Solution Approach 1:
The patent segments the training process into multiple tiers: first training on relevance labels, then training on user click data for swappable results. This segmentation allows the system to maintain simplicity in the first tier while adding discrimination capability in the second tier without overwhelming complexity.
Solution Approach 2:
The patent adds another dimension to the training data by incorporating user click data alongside relevance labels. This dimensional expansion provides additional signal for distinguishing results with the same relevance label, transforming a 1D problem into a 2D training space that captures both relevance and user preference.
2Device complexity
If conventional algorithms rely solely on assigned relevance labels, then data processing complexity is reduced, but user intent capture capability deteriorates
Solution Approach 1:
The patent makes the training system multi-functional by using user click data to serve dual purposes: maintaining relevance label-based training while simultaneously capturing user intent. The same training framework handles both relevance discrimination and intent capture, increasing reliability without proportionally increasing complexity.
Solution Approach 2:
The patent incorporates user click data as feedback signal that informs the training process. User clicks provide implicit feedback about preference among equally relevant results, allowing the model to adjust and better capture user intent while keeping the overall system architecture relatively simple.
3Measurement precision
If multi-tiered training with multiple ranking metrics is implemented, then ranking model accuracy is improved, but training complexity increases
Solution Approach 1:
The multi-tiered training is segmented into distinct phases: first tier uses relevance labels with one ranking metric, second tier uses user click data with additional ranking metrics. This segmentation allows the system to achieve higher accuracy progressively while managing complexity in controllable stages rather than all at once.
Solution Approach 2:
The patent performs preliminary training on relevance labels before incorporating user click data and additional ranking metrics. This preliminary action establishes a baseline model that is then refined with additional complexity, allowing the system to build accuracy progressively while keeping initial training relatively simple.
Data Source
AI summary
Methods and systems for multi-tiered information retrieval training are disclosed. A method includes identifying results in a ranked ordering of results that can be swapped without changing a score determined using a first ranking quality measure, determining a first vector and at least one other vector for each identified swappable result in the ranked ordering of results based on the first ranking quality measure and at least one other ranking quality measure respectively, and adding the first vector and the at least one other vector for each identified swappable result in the ranked ordering of results to obtain a function of the first vector and the at least one other vector. Access is provided to the function of the first vector and the at least one other vector for use in the multi-tiered information retrieval training.