Personalized Search Proximal Neighborhood Crawl
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Solution Overview
Problem
Conventional personalized search systems fail to effectively discover new, unseen information relevant to a user's current information retrieval goals, as they primarily focus on retrieving previously accessed information based on user profiles without considering topically related but unexplored documents.
Innovation Solution
The system determines a user's profile by analyzing their history and identifies a 'proximal neighborhood' of topically related documents within a threshold link distance, which are then searched and ranked based on user profile information to provide personalized search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional personalized search systems retrieve previously accessed information based on user profiles, then user-specific information retrieval is improved, but discovery of new-unseen information relevant to current goals deteriorates
Solution Approach 1:
The patent segments the information retrieval task into two distinct components: a personal information retrieval component that retrieves previously accessed documents based on user profile, and a proximal search component that discovers new-unseen information by crawling links from previously accessed documents. This segmentation allows each component to optimize for its specific function without interfering with the other.
Solution Approach 2:
The patent introduces an intermediary mechanism - the proximal neighborhood crawl - that bridges between previously accessed documents and new potential information. By crawling links at a proximal distance from previously accessed documents, the system mediates between the user's historical information access patterns and the broader information space, enabling discovery of relevant new information while maintaining personalization.
2Adaptability or versatility
If the system crawls links at proximal distance from previously accessed documents, then discovery of relevant new information is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic crawling parameters that can be adjusted based on user needs and system state. The crawl depth, breadth, and other parameters are not fixed but can be dynamically modified to balance between discovery effectiveness and system resource consumption. This dynamic approach allows the system to adapt its complexity level to the specific retrieval task.
Solution Approach 2:
The patent utilizes parameter changes to control the crawling process, including crawl depth, breadth, and other configurable parameters. By changing these parameters, the system can adjust the extent and focus of the proximal neighborhood exploration, enabling flexible control over the trade-off between discovery capability and system complexity without requiring fundamental architectural changes.
3Ease of operation
If the system integrates web and file search functions into a single system, then information retrieval convenience is improved, but focus on discovering new-unseen information deteriorates
Solution Approach 1:
The patent segments the integrated search system into distinct functional modules: one handling personal information retrieval from previously accessed documents, another performing proximal crawling to discover new information, and another managing file and web search integration. This segmentation allows each module to maintain its specific focus while benefiting from the integrated system's convenience.
Solution Approach 2:
The patent creates a multi-functional search system that simultaneously provides personal information retrieval, proximal neighborhood exploration, file search, and web search capabilities. The system is designed to perform multiple functions without compromising any single function's effectiveness, achieving both ease of operation through integration and productivity through specialized sub-functions.
Data Source
AI summary
Techniques are presented to provide personalized search results to a user. A user history optionally classified into projects or tasks is determined. A profile for the user is determined by identifying keywords, concepts or other user and/or task specific descriptors within user history documents. A proximal neighborhood based on the user's history and adjustable crawling parameters is determined. The adjustable crawling parameters define which documents linked to documents in the user history are included within the proximal neighborhood. A user query is determined and used to search the set of documents within the proximal neighborhood. The results are then ranked based on the user profile information. The ranked results are optionally displayed to the user.