Discriminative Classifier for Search Query Ranking
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Solution Overview
Problem
Inverted index search techniques in search engines struggle to effectively rank results for queries with limited domains, such as finding specific types of businesses in specific locations, as they fail to distinguish the importance of index terms in local versus global contexts.
Innovation Solution
A discriminative classifier is employed to process queries, trained with artificial examples to assign optimal weights to search terms, using techniques like Support Vector Machines or Logistic Regression, which maximizes separation between classes and provides a scoring algorithm to uniquely identify documents based on their features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inverted index search techniques are used, then search speed and ability to handle large numbers of documents is improved, but the ability to accurately rank results for queries with limited domains deteriorates
Solution Approach 1:
The patent segments the search process into two distinct phases: (1) an inverted index search phase for rapid document retrieval, and (2) a discriminative classifier phase for accurate ranking. This segmentation allows each component to specialize - the inverted index handles speed while the classifier handles precision, resolving the contradiction between search speed and ranking accuracy.
Solution Approach 2:
The discriminative classifier acts as an intermediary between the inverted index search results and the final ranked output. It takes the raw results from the inverted index and transforms them into accurately ranked results by learning the relative importance of different terms in specific contexts, thus mediating between speed-oriented retrieval and precision-oriented ranking.
2Quantity of substance
If inverted index search techniques are used, then handling of large document volumes is improved, but the ability to distinguish term importance in local versus global contexts deteriorates
Solution Approach 1:
The discriminative classifier implements local quality by learning context-specific term weights that adapt to different query types and domains. Instead of using uniform term importance across all contexts, the classifier learns different weightings for terms depending on the local context (e.g., local vs. global search), enabling the system to handle large document volumes while maintaining contextual adaptability.
Solution Approach 2:
The system dynamically adjusts term importance weights based on the specific query and context. The discriminative classifier learns to dynamically reweight terms for different search scenarios (local business search vs. general information search), allowing the system to adapt to varying document volumes and query types while maintaining high relevance.
3Device complexity
If traditional search algorithms are used, then system simplicity is maintained, but the ability to provide discriminative ranking for specific domains deteriorates
Solution Approach 1:
The discriminative classifier is pre-trained offline on domain-specific data before deployment. This preliminary action allows the classifier to learn complex domain-specific patterns and term importances in advance, so that during actual search operations, the added complexity is minimal - the classifier simply applies its learned knowledge to rank results, providing high discriminative precision without significantly increasing online system complexity.
Solution Approach 2:
The patent uses training data that copies real search patterns and user behavior to create artificial examples for training the classifier. By copying actual query-document pairs and their relevance judgments, the system learns to replicate human ranking preferences, achieving high discriminative precision while keeping the core search architecture relatively simple.
Data Source
AI summary
A method and apparatus for using a classifier for processing a query are disclosed. For example, the method receives a query from a user, and processes the query to locate one or more documents in accordance with a search engine having a discriminative classifier, wherein the discriminative classifier is trained with a plurality of artificial query examples. The method then presents a result of the processing to the user.