Search Relevance via User Tag and Bookmark Analysis
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Solution Overview
Problem
Traditional search engines face challenges in delivering accurate and relevant search results due to the vast amount of internet data and user query ambiguity, as they rely primarily on keyword matching without considering user-generated tags and bookmarks that indicate human interest and relevance.
Innovation Solution
A system that analyzes user-generated tags and bookmarks to determine the relevance of web pages and objects, using methods such as term-object pair scoring, confidence ratings, and statistical algorithms to organize search results and improve their accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword matching is used to search web pages, then the search process is simple and fast, but the relevance and accuracy of search results deteriorates due to vast internet data and query ambiguity
Solution Approach 1:
The patent introduces user-generated tags and bookmarks as intermediary elements between search queries and web pages. These tags serve as mediators that capture user intent and preferences, enabling more accurate matching without fundamentally changing the search engine's core architecture. The system processes these intermediary signals to rank results, resolving the contradiction by adding a layer of semantic understanding without excessive complexity.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions with search results (clicks, bookmarks, tags) are continuously collected and used to refine future search results. This feedback loop allows the system to learn from user behavior and improve relevance over time, addressing the precision issue while managing complexity through iterative optimization rather than complete system redesign.
2Measurement precision
If user-generated tags and bookmarks are incorporated to improve search relevance, then the accuracy of search results improves, but the complexity of processing and analyzing user data increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and indexing user-generated tags and bookmarks before search queries are executed. User preferences, tagging patterns, and bookmarking behaviors are analyzed in advance and stored in optimized data structures, allowing the search system to quickly retrieve and apply this information without performing complex analysis during the actual search operation.
Solution Approach 2:
The patent segments the search process into distinct components: traditional keyword matching, tag-based relevance scoring, and bookmark-based ranking. Each component processes specific types of data independently and contributes to the final result through weighted combination. This segmentation allows the system to handle user-generated data complexity in isolated modules rather than overwhelming the entire search pipeline.
3Reliability
If confidence ratings and user preferences are weighed to determine relevance, then the user-centric quality of search results improves, but the computational requirements and processing time increase
Solution Approach 1:
The patent applies partial action by computing confidence ratings and user preference weights for only the most relevant candidate results rather than all possible matches. The system performs full-weighted analysis on a limited subset of top-ranked pages, while applying simplified or pre-computed weights to other results. This approach maintains high quality for the most important results while reducing overall computational burden and processing time.
Data Source
AI summary
A method of determining relevancies of objects to a search query includes associating multiple tags with multiple objects, recording bookmarks to the multiple objects, or both, and determining a relevance score for each of the multiple objects and a search query. One embodiment of the method combines full-text relevance algorithms with tag relevance algorithms. Other embodiments include statistical relevance algorithms such as statistical classification or rank regression algorithms. When a user executes a search query, a results list containing the objects is returned, with the objects organized based on the relevance scores. The objects are organized by, for example, listing those with the highest relevance scores first or by marking them with an indication of their relevance. Preferably, relevance scores for a tag-object pair are based on a number of times a term in the tag has been associated with the object, a number of tags associated with the object, a number of times that the tag has been associated with the multiple objects, a number of tag-object pairs that contain a term in the tag, a number of tag-object pairs that contain a reference to the object, or any combination of these.