Automated Ranking Engine Weight Tuning via Simulated Annealing
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Solution Overview
Problem
Existing web ranking systems require extensive manual tuning of parameters, leading to high overhead and long times to find optimal settings, and objective metrics often change across different use cases, making it difficult to achieve optimal ranking results.
Innovation Solution
A method that automatically adjusts weight values for ranking engines using a stochastic optimization framework combining simulated annealing and the Nelder-Mead method, allowing for the generation of optimized parameter sets to improve ranking relevance, with the ability to escape local optima and converge to global optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning of parameters is performed by domain experts, then the ranking system can achieve optimized parameters, but the overhead is very high and the time needed is very long
Solution Approach 1:
The system performs self-service by automatically tuning parameters using reinforcement learning agents that interact with the ranking engine and self-evaluate results, eliminating the need for manual domain expert intervention while maintaining optimization quality
Solution Approach 2:
The patent replaces the mechanical manual tuning process with an automated computational system using reinforcement learning algorithms, information retrieval evaluation metrics, and automated experimentation frameworks to perform parameter optimization
2Measurement precision
If numerous manual tuning iterations are performed, then optimal parameters may be found, but the overhead is very high
Solution Approach 1:
The automated system performs self-service by using reinforcement learning agents to automatically conduct tuning iterations, evaluate results using information retrieval metrics, and refine parameters without requiring complex manual coordination or domain expert intervention at each iteration step
Solution Approach 2:
The patent creates a universal automated tuning framework that can handle multiple objective metrics (precision, recall, F1 score, nDCG, MAP) and different ranking scenarios through a single multi-functional system, reducing the need for separate tuning processes for each metric
3Adaptability or versatility
If objective metrics are changed for different use cases, then the ranking system can adapt to different scenarios, but it becomes difficult to achieve optimal ranking results
Solution Approach 1:
The patent implements a universal reinforcement learning framework that can handle multiple objective metrics and different use cases through a single multi-functional system, allowing the same automated tuning process to optimize for precision, recall, F1 score, nDCG, MAP, and other metrics across various ranking scenarios
Solution Approach 2:
The system dynamically changes parameters based on the specific use case and objective metric being optimized, allowing the reinforcement learning agents to adapt their tuning strategy according to the required performance measure, whether it be precision, recall, or other information retrieval metrics
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and flexible optimization of parameter weights, reducing the likelihood of local optima convergence and achieving high relevance in web object ranking, such as search results and advertisements, by iteratively adjusting and refining weight values based on relevance metrics like nDCG and MAP.
Implementation Method 1
the transforming of the simplex is dependent on a variable temperature, which decreases for one or more transformation operations based on a simulated annealing technique
Data Source
AI summary
Disclosed are apparatus and methods for facilitating the ranking of web objects. The method includes automatically adjusting a plurality of weight values for a plurality of parameters for inputting into a ranking engine that is adapted to rank a plurality of web objects based on such weight values and their corresponding parameters. The adjusted weight values are provided to the ranking engine so as to generate a ranked set of web objects based on such adjusted weight values and their corresponding parameters, as well as a particular query. A relevance metric (e.g., that quantifies or qualifies how relevant the generated ranked set of web objects are for the particular query) is determined. The method includes automatically repeating the operations of adjusting the weight values, providing the adjusted weight values to the ranking engine, and determining a relevance metric until the relevance metric reaches an optimized level, which corresponds to an optimized set of weight values. The repeated operations utilize one or more sets of weight values including at least one set that results in a worst relevance metric value, as compared to a previous set of weight values, according to a certain probability in order to escape local optimal solution to reach the global optimal solution.