Dynamic Search Range Adjustment for Intent-Based Query Processing
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Solution Overview
Problem
Conventional information processing systems often provide irrelevant search results by including store names in search queries, leading to products from unrelated stores being displayed as search results, due to the lack of precise targeting of user intentions in keyword searches.
Innovation Solution
An information processing device that utilizes click-through rate (CTR) analysis and natural language processing to determine if a store name is included in the search query, dynamically adjusting the search range to prioritize either store-specific or product-specific results based on user intent, using machine learning models like deep neural networks to differentiate between store and product searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If store names are included in search queries to provide comprehensive results, then the quantity of search results increases, but the relevance and accuracy of search results deteriorates
Solution Approach 1:
The patent implements dynamic search range adjustment based on CTR analysis. The system automatically expands or contracts the search scope depending on whether the query contains store names, transitioning between a first search range (when store names are present) and a second search range (when store names are absent). This dynamic adaptation resolves the contradiction by adjusting the quantity of results based on query characteristics while maintaining accuracy.
Solution Approach 2:
The system changes the search parameter (search range) based on the detected presence of store names in queries. By using NLP to identify store names and adjusting the search scope accordingly, the system optimizes both result quantity and accuracy for different query types, resolving the trade-off between comprehensive results and precise matching.
2Adaptability or versatility
If the search range is expanded to include more information, then the completeness of search results improves, but the relevance to user intent deteriorates
Solution Approach 1:
The system dynamically adjusts the search range based on CTR analysis results. When store names are detected in queries, the system contracts the search range to prioritize store-specific results, whereas without store names, it expands to provide broader product search results. This dynamic behavior ensures both completeness and relevance are optimized according to user intent.
Solution Approach 2:
The patent segments the search space into different ranges (first search range and second search range) based on query characteristics. By dividing the search scope and selectively applying different ranges, the system maintains completeness for general searches while ensuring relevance for store-specific queries, resolving the contradiction between adaptability and reliability.
3Device complexity
If conventional keyword search is used without analyzing user intent, then the simplicity of the search system is maintained, but the precision of matching user needs deteriorates
Solution Approach 1:
The patent introduces CTR analysis and NLP processing as intermediary steps between the user query and the search execution. These intermediaries analyze the query to detect store names and determine user intent, then adjust the search range accordingly. This intermediary layer improves precision without significantly increasing system complexity, as it builds upon existing search infrastructure.
Solution Approach 2:
The system uses CTR (click-through rate) analysis as feedback to optimize search results. By analyzing historical click data and using it to adjust search ranges in real-time, the system improves user intent matching precision while maintaining operational simplicity. The feedback mechanism automatically refines search behavior without requiring complex manual configuration.
Data Source
AI summary
An information processing device according to the present application includes an acquisition unit, a selection unit, and a search unit. The acquisition unit acquires a search query input by a user. The selection unit selects a type of target information to be searched on the basis of the search query acquired by the acquisition unit. The search unit searches for information corresponding to the search query from the type of target information to be searched selected by the selection unit.