Location Disambiguation via Gradient Boosted Decision Trees
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Solution Overview
Problem
Existing location disambiguation methods for search engines face challenges in accurately resolving ambiguous location references in local searches, particularly due to the combinatorial explosion problem and the lack of contextual information in short search queries, leading to errors in identifying the intended geographic location.
Innovation Solution
A data-driven approach using gradient boosted decision trees to build location models based on contextual and non-contextual features from search query logs and geographic databases, which automatically generates decision rules for location disambiguation, incorporating features like user location and search history to provide personalized results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rule-based location disambiguation methods are used, then the system can resolve some ambiguous location references, but the error rate increases and reliability decreases due to combinatorial explosion and lack of contextual information
Solution Approach 1:
The patent replaces traditional rule-based mechanical systems with a machine learning model (gradient boosted decision trees) that automatically learns disambiguation patterns from data. This substitution eliminates the need for manual rule creation and maintenance while improving accuracy through data-driven insights.
Solution Approach 2:
The patent transforms the location disambiguation problem from a rule-based classification task to a parameter-based machine learning task. By changing parameters such as user location, search history, and contextual features into numerical inputs for the model, the system achieves more reliable disambiguation without manual rule complexity.
2Measurement precision
If comprehensive contextual features are collected and processed, then location disambiguation accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent pre-processes and stores contextual features (user location, search history, business categories) in structured formats before they are needed for disambiguation. This preliminary organization allows the machine learning model to quickly access and process features during actual search queries, reducing processing time while maintaining comprehensive analysis.
Solution Approach 2:
The patent creates simplified representations (copies) of complex contextual information that can be quickly processed. Instead of analyzing raw search logs and geographic databases in real-time, the system pre-generates feature vectors and statistical summaries that serve as efficient copies for rapid model inference.
3Adaptability or versatility
If personalized features like user location and search history are incorporated, then search results become more relevant and accurate, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent designs a unified machine learning model that handles multiple functions: traditional location disambiguation, personalized recommendation, and contextual relevance ranking. This universal approach consolidates what could be separate complex systems into a single versatile model, reducing overall system complexity while maintaining personalization capabilities.
Solution Approach 2:
The patent segments the personalization data processing into distinct feature extraction modules (user location features, search history features, contextual features) that feed into the machine learning model. This segmentation allows each module to be optimized independently while maintaining overall system efficiency and manageability.
Data Source
AI summary
Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries.