Search Query Analysis for User Affinity Filtering
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Solution Overview
Problem
Current search engines do not utilize user affinity when delivering search results, leading to users having to manually sift through both favorable and unfavorable information, especially in cases of biased or polarized searches.
Innovation Solution
A system that analyzes search queries by evaluating user social media activity and previous search patterns to determine user affinity, using language parsing and analysis techniques to filter search results based on the user's bias and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines return all information matching search query terms without filtering, then the quantity of search results is maximized, but the user must manually parse through both favorable and unfavorable information, reducing efficiency and user experience
Solution Approach 1:
The system performs preliminary analysis of user affinity indicators (social media activity, browsing history, search patterns) before generating search results. This preliminary action enables the system to pre-filter results according to user preferences, eliminating the need for users to manually parse through unfavorable information while maintaining comprehensive result coverage.
Solution Approach 2:
The system extracts and isolates search results that align with user affinity from the complete set of matching results. By separating favorable information from unfavorable information based on analyzed user preferences, the system presents only the extracted relevant portion to the user, reducing manual parsing time while preserving access to all matching results if needed.
2Reliability
If search engines provide unbiased search results matching all query terms, then objectivity is maintained, but users seeking information supporting their specific viewpoint must manually identify favorable information among conflicting results
Solution Approach 1:
The system applies different quality characteristics to different portions of search results based on user affinity. Rather than uniformly treating all results, it identifies and highlights portions of results that align with user preferences while maintaining access to the complete unbiased result set. This local differentiation makes favorable information easily identifiable without compromising overall objectivity.
Solution Approach 2:
The system introduces an intermediary layer between the unbiased search results and the user. This intermediary analyzes user affinity indicators and mediates the presentation of results by organizing, highlighting, or filtering information based on user preferences, thereby easing the user's task of finding favorable information while preserving the underlying objectivity of the source material.
3Adaptability or versatility
If the system analyzes user social media activity and search patterns to determine user affinity, then search results can be filtered to match user preferences, but the complexity of the search system increases
Solution Approach 1:
The system employs multi-functional components that perform multiple tasks. The same analytical mechanisms used for other search optimizations (query understanding, result ranking) are extended to also determine user affinity. By making existing components multi-functional rather than adding entirely separate systems, the patent achieves adaptability to user preferences while minimizing the increase in overall system complexity.
Data Source
AI summary
According to embodiments of the present invention, machines, systems, computer-implemented methods and computer program products for retrieving information pertaining to an affinity of a user are provided. In some embodiments, a search query is received from a user. The search query is analyzed to determine a bias of the user. The social media activity of the user is evaluated to determine affinity indicators for the user. Prior searches and selection of search results by the user is evaluated to detect patterns of the user. An affinity of the user is determined based on the bias, affinity indicators, and patterns. Initial search results are generated that satisfy the search query, and the initial search results are filtered based on the determined affinity of the user to produce search results in accordance with the determined affinity of the user.