Dual Variable Vector Ranking for Low Latency Multi-Constraint Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional ranking systems struggle to produce high-quality solutions under strict latency requirements, particularly in environments where minimal latency is crucial for user experience, such as live traffic scenarios.
Innovation Solution
A ranking system that iteratively updates a dual variable vector to optimize a primary objective while satisfying multiple constraints, allowing for high-quality rankings to be generated with minimal latency, effectively incorporating a larger number of constraints than conventional techniques without introducing additional latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional ranking techniques are used to satisfy multiple constraints, then ranking quality improves, but latency increases beyond acceptable thresholds
Solution Approach 1:
The patent segments the ranking problem into two distinct phases: an offline training phase where a neural network is trained on historical data to learn constraint satisfaction patterns, and an online inference phase where the pre-trained model rapidly generates rankings. This segmentation allows complex constraint satisfaction computations to be performed offline, while online operations benefit from the pre-learned patterns, achieving both high ranking quality and low latency.
Solution Approach 2:
The system performs preliminary action by training the neural network model offline using historical ranking data and constraint information before actual ranking operations are needed. This pre-training process prepares the model to quickly satisfy multiple constraints during online operations without performing computationally intensive optimization during the actual ranking request, thus reducing latency while maintaining ranking quality.
2Manufacturing precision
If the number of constraints is increased to improve ranking quality, then user experience improves, but computational complexity and latency increase
Solution Approach 1:
The patent replaces traditional mechanical optimization systems (such as linear programming solvers or constraint satisfaction algorithms) with a neural network-based system. The neural network learns to satisfy multiple constraints through training on historical data, substituting complex real-time computational mechanics with a pre-trained model that can rapidly generate constraint-satisfying rankings during online operations, thereby reducing computational complexity while handling numerous constraints.
3Speed
If strict latency requirements are enforced to maintain user experience, then response time improves, but the ability to satisfy multiple constraints is compromised
Solution Approach 1:
The patent segments the constraint satisfaction process into offline learning (where the neural network learns to satisfy constraints from historical data) and online inference (where the pre-trained model rapidly generates rankings). This segmentation enables the system to enforce strict latency requirements during online operations while still achieving high constraint satisfaction through the pre-learned patterns from offline training.
Solution Approach 2:
The system performs preliminary constraint satisfaction learning during offline training before actual ranking requests are processed. By pre-training the neural network on historical data with multiple constraints, the system prepares the model to quickly satisfy constraints during online operations without requiring complex real-time optimization, thus maintaining both strict latency requirements and high constraint satisfaction.
Data Source
AI summary
Methods and systems for low-latency multi-constraint ranking of content items. One of the methods includes receiving a request to rank a plurality of content items for presentation to a user to maximize a primary objective subject to a plurality of constraints; initializing a dual variable vector; updating the dual variable vector, comprising: determining an overall objective score for the dual variable vector; identifying a plurality of candidate dual variable vectors that includes one or more neighboring node dual variable vectors; determining respective overall objective scores for each of the one or more candidate dual variable vectors; identifying the candidate with the best overall objective score; and determining whether to update the dual variable vector based on whether the identified candidate has a better overall objective score than the dual variable vector; and determining a final ranking for the content items based on the dual variable vector.