Multi-Stage Content Evaluation Pipeline for Model Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Selecting relevant content for users from a large number of content items is challenging due to the complexity of models in the content evaluation pipeline, leading to loss of relevant content at earlier stages and suboptimal selection for display.
Innovation Solution
A multi-stage content evaluation pipeline with increasingly complex models, where each stage selects a decreasing number of content items to pass to the next stage, with a pipeline training module that trains models to mimic the output of subsequent stages to optimize content selection, ensuring the most high-value content items are selected for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex model is used at an early stage in the content evaluation pipeline, then the accuracy of content selection is improved, but the computational resources and time required are increased
Solution Approach 1:
The content evaluation pipeline is divided into multiple stages, each with models of increasing complexity. The first stage uses a simpler model to filter the largest portion of content, while subsequent stages use progressively more complex models on smaller subsets. This segmentation allows the system to achieve high overall accuracy without requiring every content item to be evaluated by the most complex model, thus reducing total computational time.
Solution Approach 2:
Simpler models perform preliminary filtering of content items before they reach the more complex models. This preliminary action eliminates obviously irrelevant content early in the pipeline, so that the complex models only need to evaluate a small fraction of the original content set, significantly reducing their computational burden and overall processing time.
2Productivity
If a simple model is used at an early stage in the content evaluation pipeline, then the computational resources are reduced, but relevant content items may be filtered out
Solution Approach 1:
The evaluation pipeline segments content processing across multiple stages with increasing model complexity. Early stages use simpler models for broad filtering, while later stages use complex models to evaluate the remaining content in greater detail. This ensures that no potentially relevant content is permanently lost, as the multi-stage approach allows progressive refinement of selections.
Solution Approach 2:
The pipeline provides feedback mechanisms where the output of one stage becomes the input for the next stage with a more sophisticated model. This feedback loop ensures that content items that pass initial simpler filters are given another chance at selection by more complex models, reducing the risk of relevant content being incorrectly filtered out early in the process.
3Measurement precision
If multiple complex models are used in sequence, then the content selection accuracy is improved, but the device complexity is increased
Solution Approach 1:
The system segments the evaluation task across multiple stages, each handling a specific portion of the content filtering process. This segmentation of the evaluation function allows the use of multiple models with different complexity levels appropriate to each stage's needs, rather than using a single uniformly complex model throughout, thereby managing overall system complexity while maintaining high accuracy.
Data Source
AI summary
To present one or more content items to users of an online system, the online system identifies a content evaluation pipeline including an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item. The content evaluation pipeline selects a decreasing number of content items, from each stage of the order, according to the order of the stages. The online system optimizes the selection of content items selected at the plurality of stages of the content evaluation pipeline by training the computer models to predict content selection values that the subsequent model would generate for a content items in a training data set and content items that the subsequent model would select for input to the next stage of the content evaluation pipeline.