Neural Network Ranking via Pairwise Loss Training
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Solution Overview
Problem
Conventional ranking systems using pointwise losses struggle with rank invariance issues, resulting in low variance ranking score distributions that fail to accurately represent the order of elements, especially when dealing with large datasets such as search results or software applications, as they do not consider relative positions of elements.
Innovation Solution
A ranking system that trains neural networks using both pointwise and pairwise losses, generating training pairs based on relative positions of elements in a presentation order, allowing for the update of network parameters to improve ranking accuracy by incorporating relative relationships between elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pointwise losses are used for training neural networks, then the training process is simple and computationally efficient, but the ranking accuracy deteriorates due to rank invariance issues and low variance in ranking score distributions
Solution Approach 1:
The patent combines pointwise loss and pairwise loss functions into a unified training objective. The neural network is trained simultaneously on individual query-document pairs (pointwise) and on pairs of document pairs (pairwise), merging two different loss computation approaches to achieve both training efficiency and ranking accuracy.
Solution Approach 2:
The patent introduces an intermediary pairwise loss computation that operates on pairs of training examples. This intermediary layer processes the relationship between positive and negative examples, generating a pairwise loss that mediates the training signal to improve ranking discrimination without replacing the efficient pointwise training approach.
2Productivity
If only pointwise losses are used, then the training pipeline is simple and fast, but the network fails to capture relative positions of elements, resulting in poor ranking discrimination
Solution Approach 1:
The patent applies partial pairwise loss computation by selectively processing pairs of training examples rather than computing all possible pairs. This partial action approach maintains training speed by computing pairwise losses only for relevant positive-negative example pairs, rather than exhaustively comparing all examples.
3Measurement precision
If pairwise losses are incorporated into the training pipeline, then ranking accuracy improves by considering relative positions, but the training complexity and computational cost increase
Solution Approach 1:
The patent segments the training process into distinct pointwise and pairwise loss computation stages. Each stage processes different aspects of the training data independently, with the pointwise loss handling individual example classification and the pairwise loss handling relative ordering. This segmentation allows for modular implementation and efficient computation of each loss component.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to generate a ranking score for a network input. One of the methods includes generating training data and training the neural network on the training data. The training data includes a plurality of training pairs. The generating comprising: obtaining data indicating that a plurality of training network inputs were displayed in a user interface according to a presentation order, obtaining data indicating that a first training network input of the plurality of training network inputs has a positive label, determining that a second training network input of the plurality of training network inputs (i) has a negative label and (ii) is higher than the first training network input in the presentation order, and generating a training pair that includes the first training network input and the second training network input.