ML Ranking Engines for Search Result Segmentation
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Solution Overview
Problem
Existing ranking systems in machine learning struggle to effectively combine search results from different machine learning model execution engines for various search result types, leading to suboptimal search outcomes.
Innovation Solution
A computing system and method that determine multiple machine learning model execution engines based on a search query and different search result types, generating subsets of search results from each engine, and combining them to produce a comprehensive set of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning model execution engine is used for ranking, then the system complexity is low, but the accuracy and relevance of search results deteriorate
Solution Approach 1:
The patent segments the ranking function into multiple specialized machine learning model execution engines, each optimized for specific search result types (e.g., product results, video results, article results). This segmentation allows each engine to achieve higher precision for its specialized domain while the overall system manages complexity through modular architecture with a central coordinator that routes queries to appropriate engines.
Solution Approach 2:
The patent implements a universal ranking system that can handle multiple search result types through a coordinated ensemble of specialized engines. The system provides multi-functionality by allowing a single ranking infrastructure to serve diverse search scenarios (e-commerce, media, news, etc.) while maintaining specialized optimization for each type through dedicated model execution engines.
2Measurement precision
If multiple machine learning model execution engines are used for different search result types, then the accuracy and relevance of search results improve, but the device complexity increases
Solution Approach 1:
The patent introduces a central coordinator as an intermediary component that manages the complexity of multiple specialized machine learning model execution engines. This coordinator receives search queries, determines the appropriate search result types, routes queries to the relevant specialized engines, and aggregates the results. This intermediary layer shields the system from complexity while enabling the benefits of multiple specialized engines.
Solution Approach 2:
The system segments the ranking functionality into distinct specialized engines for different search result types (product, video, article, etc.), allowing each segment to be optimized independently. This segmentation improves accuracy for specific domains while the modular structure manages overall system complexity through clear separation of concerns and independent deployment of each engine type.
3Productivity
If multiple machine learning model execution engines are deployed, then the productivity of search result generation improves, but the loss of time for result combination increases
Solution Approach 1:
The patent applies preliminary action by having the central coordinator determine the appropriate search result types and select the corresponding specialized machine learning model execution engines before the actual ranking computation. This pre-determination and pre-routing of queries to appropriate engines reduces the time required for result combination, as the system avoids unnecessary computation from irrelevant engines and can parallelize processing of multiple result types simultaneously.
Data Source
AI summary
Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning ranking. An example computing method includes receiving a search query and determining a plurality of machine learning model execution engines based on the search query and a plurality of search result types. The example computing method further includes generating a plurality of subsets of search results based on the search query and the plurality of machine learning model execution engines. The example computing method further includes generating a set of search results comprising at least one search result from each of the plurality of subsets of search results.