Information Handling System Search Result Relevance
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Solution Overview
Problem
Information handling systems face challenges in providing relevant search results as they lack the ability to effectively incorporate user context and behavioral data to adjust search query strategies dynamically, leading to suboptimal results.
Innovation Solution
An information handling system that includes a processing device capable of generating probabilistic strategies based on user context and behavioral data, using signal and strategy emitter modules to alter search results in real-time, by analyzing past interactions and user preferences to determine the likelihood of specific search intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search results are provided without incorporating user context and behavioral data, then the search system is simple and fast, but the relevance and accuracy of search results deteriorate
Solution Approach 1:
The system performs preliminary analysis of user context and behavioral data before generating search results. User profiles, search history, and interaction patterns are preprocessed to create probabilistic strategies that can be quickly applied during search query processing, improving relevance without significant real-time computational overhead
Solution Approach 2:
The system changes parameters by generating probabilistic strategies based on user context and behavioral data. Instead of using fixed search algorithms, the system adjusts search behavior dynamically based on calculated probabilities of user intent, allowing the same infrastructure to adapt to different user needs and improve result relevance
2Adaptability or versatility
If search results are provided without real-time adjustment based on user context, then the system is simple and reliable, but the adaptability to user needs deteriorates
Solution Approach 1:
The system introduces dynamics by generating probabilistic strategies that adapt to user context and behavioral data in real-time. The search system transitions from static, fixed algorithms to dynamic behavior adjustment based on calculated probabilities, allowing the system to adapt to changing user needs while maintaining stable operation through probabilistic rather than deterministic adjustments
Solution Approach 2:
The system incorporates feedback by analyzing user interactions with search results and adjusting future search behaviors accordingly. User context and behavioral data provide feedback loops that continuously refine probabilistic strategies, improving adaptability to user needs while maintaining reliability through data-driven rather than speculative adjustments
Data Source
AI summary
An information handling system includes a memory to store combined behavioral data and interactional data associated with search queries, and a processing device. The processing device communicates with the memory. The processing device receives a first search query including first search terms, and user context associated with the first search query, retrieves the combined behavioral data and interactional data from previous search queries, analyzes the first search terms using the combined behavioral data and interactional data, generates signals based on the first search terms, the user context, and the combined behavioral data and interactional data, and provides the signals for use in altering results to be provided in response to the first search query.