Multi-objective Search Ranking via Augmented Lagrangian Optimization
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Solution Overview
Problem
Current machine learning solutions struggle to systematically handle multiple objectives in search result ranking, leading to tedious and time-consuming hand-tuning of parameters, resulting in sub-optimal solutions, especially when conflicting objectives are involved.
Innovation Solution
The implementation of an Augmented Lagrangian-based method within the Gradient Boosting Tree framework, specifically in the LambdaMART algorithm, to systematically optimize search result ranking by converting constrained optimization problems into unconstrained ones using penalty terms and iteratively optimizing dual variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hand-tuning parameters is used to meet performance goals in multi-objective ranking, then model development time is reduced, but the solution quality becomes sub-optimal and tedious
Solution Approach 1:
The system performs automatic parameter tuning through iterative optimization algorithms that self-adjust ranking parameters to satisfy multiple objectives simultaneously, eliminating the need for manual hand-tuning while achieving optimal solutions
Solution Approach 2:
The iterative optimization process uses feedback from multiple objective functions to continuously adjust and refine ranking parameters, allowing the system to learn from performance metrics and automatically converge to optimal parameter settings
2Reliability
If multiple conflicting objectives are considered in ranking, then model performance improves, but parameter tuning becomes more complex and time-consuming
Solution Approach 1:
The system merges multiple conflicting objective functions into a unified optimization framework that simultaneously considers all objectives, using combined gradient information to update ranking parameters in a coordinated manner rather than tuning each objective separately
Solution Approach 2:
The optimization algorithm serves multiple functions by handling diverse objective types (relevance, diversity, fairness, business constraints) through a single unified framework, making the system adaptable to various ranking scenarios without requiring separate tuning procedures
3Productivity
If automated optimization methods are implemented, then model refresh efficiency improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary computations by pre-calculating gradients and optimization paths during training, storing intermediate results that can be quickly applied during model refresh, reducing the computational burden during actual deployment updates
Data Source
AI summary
Devices and techniques are generally described for ranking of search results based on multiple objectives. A first ranking for a plurality of search results is determined using a first machine learning model optimized for a first objective for ranking search results. A second objective for ranking search results is determined. A constraint is determined for the at least one second objective. The first machine learning model is iteratively updated to generate an updated machine learning model by minimizing a cost of the first objective subject to the constraint, wherein violations of the constraint are penalized using a penalty term. A second ranking for the plurality of search results is determined using the updated machine learning model. The search results of the second ranking are reordered relative to the search results of the first ranking.