Cross-List Learning to Rank with Correlation-Weighted Training
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Solution Overview
Problem
Existing ranking systems struggle to effectively leverage information across different training lists, leading to suboptimal performance in recommendation and information retrieval systems, particularly when aggregating queries with varying relevance and engagement metrics.
Innovation Solution
A cross-list learning to rank approach that trains ranking models using pairs of items from different training examples, incorporating correlation scores to weight pairwise or listwise losses, enhancing the model's ability to capture interactions between items from similar queries or data slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ranking models are trained using only single-list data, then training simplicity is maintained, but ranking accuracy across diverse queries deteriorates
Solution Approach 1:
The patent merges multiple training lists into a unified cross-list training framework. Training examples from different lists are combined and processed together, allowing the model to learn from diverse query-item relationships across multiple lists simultaneously. This merging approach improves ranking accuracy by capturing broader patterns while managing training complexity through systematic integration.
Solution Approach 2:
The cross-list training framework serves multiple functions: it processes single-list data, cross-list data, and weighted combinations thereof. The training mechanism is designed to be universal, handling various data configurations and query types within a single unified approach, thereby improving accuracy without proportionally increasing complexity.
2Loss of information
If cross-list training with correlation scores is implemented, then information utilization across lists is improved, but computational complexity increases
Solution Approach 1:
Correlation scores serve as intermediary metrics that quantify the relationship between queries from different lists. These scores act as mediators in the training process, enabling the model to weigh and balance information from multiple lists systematically. The correlation scores facilitate efficient information utilization by providing a structured way to integrate cross-list data without requiring complex ad-hoc processing.
3Adaptability or versatility
If training data from multiple lists is aggregated, then model generalization is improved, but training time increases
Solution Approach 1:
The patent introduces weighting parameters that control the contribution of different lists and training examples. By adjusting these parameters, the training process can balance between utilizing diverse data for generalization and managing training time efficiently. The weighting mechanism allows flexible adaptation of training dynamics without requiring complete retraining from scratch.
Data Source
AI summary
Provided are systems and methods that perform learning to rank using training data for two or more different training lists. Specifically, a training dataset can include a number of training examples. Each training example can include a query and a plurality of items that are potentially responsive to the query. The ranking model can be trained using pairs of items taken from two different training examples.