Trust Network Annotation Search Ranking
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Solution Overview
Problem
Existing search systems fail to effectively incorporate individual user preferences and trust networks in ranking search results, leading to irrelevant content being prioritized and limiting the ability to share and organize relevant information effectively.
Innovation Solution
The integration of user judgment information, including annotations from a querying user's trust network, into search systems to enhance relevance and organization of search results, where a trust network is built by identifying users with whom the querying user has a trust relationship, and their annotations are used to rank and highlight relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search results are ranked using traditional algorithms based on term occurrence and link count, then the search system maintains simplicity in ranking criteria, but the relevance of results to individual user preferences deteriorates
Solution Approach 1:
Users pre-annotate documents with metadata and ratings before search queries are executed. These annotations are stored in association with user identifiers and document identifiers, allowing the search system to later retrieve and apply relevant annotations from the user's trust network without adding complexity to the real-time search process
Solution Approach 2:
User annotations serve as an intermediary layer between traditional search algorithms and final result ranking. The system retrieves annotations from the user's trust network and uses this intermediate information to adjust rankings, combining the simplicity of traditional algorithms with personalized relevance
2Reliability
If search results are personalized to individual user preferences, then the relevance to specific users improves, but the ability to share and organize information across users deteriorates
Solution Approach 1:
The annotation system serves multiple functions simultaneously: it personalizes search results for individual users while also enabling information sharing across the trust network. Annotations created by one user can be utilized by trusted users, making the system both personalized and shareable
Solution Approach 2:
The system segments annotations by user identifiers and trust relationships, allowing personalized retrieval for each user while maintaining the ability to share specific annotations with designated members of the trust network through selective access control
3Reliability
If users manually explore search hits to find relevant content, then comprehensive coverage of results is achieved, but the time required to complete searches increases
Solution Approach 1:
Users provide feedback through annotations and ratings of documents they find relevant or irrelevant. This feedback is stored and used to automatically adjust the ranking of future search results, allowing the system to learn from user interactions and improve result quality without requiring manual exploration of all hits
Solution Approach 2:
The system pre-ranks search results using annotations from the user's trust network before the user views the results. This preliminary ranking based on trusted user annotations significantly reduces the time users need to spend exploring results while maintaining high relevance
Data Source
AI summary
Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user's trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.