Search Result Prioritization via User Profile Data
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Solution Overview
Problem
Users face difficulty in identifying relevant search results from large numbers of search engine outputs, as there is no straightforward way to determine which results are of interest without reviewing all results, especially when numerous search results are returned, making it burdensome or impossible to find desired information.
Innovation Solution
A method and system that prioritize search results by using user-specific and similar users' search profiles, based on past visitation information and interests, to order search results, ensuring more relevant content is presented first, with the ability to update profiles dynamically based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines return a large number of search results to ensure comprehensive coverage, then the completeness of information retrieval is improved, but the time and effort required for users to review and find relevant information increases significantly
Solution Approach 1:
The system performs preliminary actions by collecting and storing user-specific information (search history, visited sites, bookmarks, expressed interests) before the user performs a search. This pre-collected data is then used to automatically prioritize and order search results, so that the most relevant results are presented first without requiring the user to manually review all results. This resolves the contradiction by using preliminary data collection to enable efficient result presentation.
Solution Approach 2:
The system incorporates feedback mechanisms where users can indicate which search results are of interest or not of interest. This feedback is then used to update and refine the user's search profile, improving the accuracy of future result prioritization. The feedback loop continuously improves the system's ability to present relevant results first, reducing the time users need to spend reviewing search results while maintaining comprehensive coverage.
2Quantity of substance
If search engines provide comprehensive search results without prioritization, then the completeness of information is improved, but the ease of operation for users deteriorates as they must manually review each result
Solution Approach 1:
The system applies local quality by customizing the presentation of search results based on individual user characteristics and preferences. Each user receives a personalized ranking of search results based on their unique search history, visited sites, bookmarks, and expressed interests. This localized customization makes the search results more relevant and easier to navigate for each specific user, while still providing comprehensive coverage through the underlying search engine.
Solution Approach 2:
The system enables self-service by automatically using the search engine to prioritize results based on pre-collected user information. The user's past behavior data (search history, visited sites, bookmarks) is automatically processed to generate personalized result ordering without requiring manual intervention. This self-service mechanism improves ease of operation while maintaining comprehensive result coverage.
3Reliability
If the system collects and stores extensive user-specific information to improve result prioritization, then the relevance of search results is improved, but the device complexity increases
Solution Approach 1:
The system achieves universality by using a single user profile data structure to store multiple types of information (search history, visited sites, bookmarks, expressed interests) that all serve the common purpose of improving search result prioritization. This multi-functional approach consolidates what could be multiple separate complex systems into one unified profile management mechanism, improving result relevance while controlling overall system complexity.
4Measurement precision
If users manually review search results to identify relevant information, then the accuracy of information selection is improved, but the time required for this process increases significantly
Solution Approach 1:
The system performs preliminary action by automatically analyzing user's past search behavior, visited sites, bookmarks, and expressed interests before the user needs to review search results. This pre-computed prioritization based on user profile data presents the most relevant results first, enabling users to quickly identify accurate information without manually reviewing all results, thus maintaining high accuracy while significantly reducing time requirements.
Data Source
AI summary
An enhancement to a search engine is disclosed for prioritizing search results obtained from an information search engine such as those accessible via a network (e.g., the Internet and/or a corporate intranet). In response to a user search query, the enhanced search engine of the invention prioritizes the search results using stored information indicative of network sites: (a) previously visited by the user, and/or (b) to which the user has indicated an interest (or disinterest) in the content thereof. Additionally, the invention prioritizes the search results using stored information indicative of other users that are determined to have similar searching interests. Thus, since the stored information can dynamically change, repeated performances of the same search can present different initial, more user customized, portions of the search results.