Stochastic Label Aggregation for Multi-Objective Search Ranking
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Solution Overview
Problem
Existing machine learning models for search result ranking often fail to optimize multiple objectives simultaneously, leading to suboptimal solutions that do not reach the Pareto Frontier, especially when using deterministic label aggregation methods.
Innovation Solution
The implementation of stochastic label aggregation techniques, which randomly select labels according to a given distribution over different objectives, allowing for the training of machine learning models that can generate a subset of models that cannot be dominated by any combination of models on the Pareto Frontier, thereby achieving optimal solutions for multi-objective ranking optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic label aggregation methods are used to train machine learning models for search result ranking, then the training process is simple and deterministic, but the models cannot reach the Pareto Frontier and fail to optimize multiple objectives simultaneously
Solution Approach 1:
The patent applies dynamics by transforming the static deterministic label aggregation into a dynamic stochastic process. Instead of using fixed deterministic rules to aggregate labels from multiple objectives, the system employs stochastic sampling that randomly selects labels according to a given distribution. This dynamic approach allows the training process to explore different label combinations and ultimately reach the Pareto Frontier, optimizing multiple objectives simultaneously while maintaining tractable computation through probabilistic methods.
2Manufacturing precision
If manual tuning of machine learning model parameters is performed to meet performance goals, then performance goals can be achieved, but the process is time-consuming and does not guarantee optimal multi-objective solutions
Solution Approach 1:
The patent implements self-service by enabling the machine learning model to automatically optimize its own parameters through stochastic label aggregation during training. Instead of requiring manual parameter tuning to achieve performance goals, the system uses stochastic sampling to automatically explore the parameter space and converge to optimal solutions that satisfy multiple objectives. This self-service mechanism eliminates time-consuming manual intervention while guaranteeing optimal multi-objective solutions through the probabilistic training process.
3Reliability
If multiple objectives are considered simultaneously in search result ranking, then the quality of search results improves, but the optimization problem becomes more complex and harder to solve
Solution Approach 1:
The patent applies parameter changes by transforming the multi-objective optimization problem into a single-objective problem through stochastic label aggregation. Instead of directly optimizing multiple conflicting objectives simultaneously, the system changes the parameter representation by sampling labels from different objectives according to a given distribution. This parameter transformation reduces the complexity of the optimization problem while maintaining the ability to improve search result quality by considering multiple objectives through the probabilistic label selection process.
Data Source
AI summary
Devices and techniques are generally described for ranking of search results based on multiple objectives. In various examples, a first set of search results may be determined. A first objective and a second objective for ranking the first set of search results may be determined. A first label associated with the first objective may be selected for a first training data instance. A second label associated with the second objective may be selected for a second training data instance. A first machine learning model may be generated using the first training data instance and the second training data instance. In some examples, the first machine learning model may be effective to rank the first set of search results based at least in part on the first objective and the second objective.