Machine Learning Query Classification for Head and Tail URL Index Selection
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Solution Overview
Problem
Conventional search engines face challenges in optimizing query processing due to rigid rule-based systems that fail to adapt to changing conditions, leading to sub-optimal performance and user dissatisfaction, especially when dealing with multiple indexes of varying speeds.
Innovation Solution
Implementing a machine learning-based query classification system that predicts whether a query is targeted at a head URL or a tail URL, using features like query length, frequency, and unigram probabilities to determine the appropriate index or combination of indexes for processing, thereby optimizing search engine performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a search engine uses multiple indexes with varying speeds and capacities, then the search engine can balance between quality and speed, but the complexity of determining which index to use increases
Solution Approach 1:
The system changes the parameter of query classification by using machine learning models to predict URL popularity categories (head vs tail URLs) based on query features. This automated classification approach resolves the complexity of manual index selection by using learned patterns from query data to dynamically determine which index to access.
Solution Approach 2:
The patent replaces rigid mechanical rule-based index selection with a machine learning-based predictive system. Instead of using fixed thresholds or manual rules to determine index access, the system uses trained models that process query features and output probability distributions over URL categories, enabling more adaptive and accurate index selection.
2Adaptability or versatility
If a search engine uses rigid rule-based systems for query classification, then the system is simple to implement, but the adaptability to changing conditions is limited
Solution Approach 1:
The system introduces dynamics by using machine learning models that can adapt to changing query patterns and network conditions. The classification system processes query features through learned representations and can adjust its predictions based on new data, making it dynamically adaptable rather than static like rule-based systems.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from query outcomes and adjusts its classification models accordingly. The machine learning models can be retrained or fine-tuned based on actual search results and user behavior patterns, enabling continuous improvement and adaptation to changing conditions.
3Measurement precision
If a search engine skips larger indexes for queries that could benefit from them, then processing speed is maintained, but query accuracy decreases
Solution Approach 1:
The system applies partial action by selectively accessing different indexes based on predicted query characteristics. Instead of always accessing the largest index or never accessing it, the system uses machine learning predictions to determine the appropriate level of index access - using faster smaller indexes for head URLs and slower larger indexes for tail URLs, achieving optimal balance between speed and accuracy.
Data Source
AI summary
Systems and methodologies for improved query classification and processing are provided herein. As described herein, a query prediction model can be constructed from a set of training data (e.g., diagnostic data obtained from an automatic diagnostic system and/or other suitable data) using a machine learning-based technique. Subsequently upon receiving a query, a set of features corresponding to the query, such as the length and/or frequency of the query, unigram probabilities of respective words and/or groups of words in the query, presence of pre-designated words or phrases in the query, or the like, can be generated. The generated features can then be analyzed in combination with the query prediction model to classify the query by predicting whether the query is aimed at a head Uniform Resource Locator (URL) or a tail URL. Based on this prediction, an appropriate index or combination of indexes can be assigned to answer the query.