Keyword Relevance via Life-Stream Feed Analysis
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Solution Overview
Problem
Current search technologies often yield irrelevant results due to the reliance on user-entered search terms, requiring multiple queries to find relevant information, which can lead to missed opportunities for users and businesses.
Innovation Solution
The system identifies related keywords by analyzing life-stream feeds using aggregated, user profile-based, and community-based mechanisms, providing a weighted combination of co-occurring keywords to enhance search query relevance in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search tools are used with user-entered search terms, then the search process is simple to operate, but the search result relevance is poor and multiple queries are needed
Solution Approach 1:
The system performs preliminary analysis of life-stream feeds to pre-identify and store relevant keywords and their co-occurrence patterns before the user actually performs a search. When a search query is received, the system retrieves pre-computed related keywords from this database, eliminating the need for real-time complex analysis and providing immediate relevant search results without requiring multiple queries from the user.
Solution Approach 2:
The system introduces life-stream feed data as an intermediary between the user's search query and the search results. This intermediary contains rich contextual information about keywords, their frequencies, and co-occurrence patterns from actual user behavior data. The intermediary enables the system to automatically generate relevant search terms and refine search results without direct user input, resolving the contradiction between operational simplicity and result relevance.
2Measurement precision
If multiple search queries are performed to improve result relevance, then search result quality improves, but the time required and user effort increase
Solution Approach 1:
The system performs preliminary extraction and storage of keyword co-occurrence relationships from life-stream feeds in advance. This pre-computed data is stored in a database that can be quickly queried during actual search operations. When a user submits a search query, the system immediately retrieves pre-identified related keywords and their relevance scores from the database, providing high-quality search results in a single query without requiring the user to perform multiple iterative searches.
Solution Approach 2:
The system uses feedback from life-stream feed data, which captures actual user behavior patterns and keyword usage trends, to continuously refine and update the relevance scoring of search results. This feedback mechanism allows the system to learn from real-world usage patterns and automatically adjust search result ranking, improving relevance without requiring multiple user queries or manual refinement.
3Measurement precision
If search results are made more relevant through advanced analysis, then the quality of results improves, but the system complexity increases
Solution Approach 1:
The system performs complex life-stream feed analysis and keyword extraction in advance, before actual search operations are needed. This preliminary processing includes identifying keywords, calculating their frequencies, determining co-occurrence patterns, and establishing relevance relationships. All this complex analysis is performed once during the indexing phase and stored in a optimized database structure, allowing subsequent search queries to be answered quickly with simple database lookups rather than repeating the complex analysis each time.
Solution Approach 2:
The system segments the search functionality into distinct modular components: life-stream feed collection, keyword extraction, co-occurrence analysis, relevance scoring, and search result presentation. Each component handles a specific aspect of the search process independently, making the overall complex system more manageable and maintainable. The modular architecture allows each component to be optimized separately and facilitates easier implementation and scaling.
Data Source
AI summary
Embodiments are directed towards providing related keywords determined to be relevant to a search term using life-streaming data to identify the related keywords. Relevancy is determined by performing the related keyword analysis when life-stream services have been updated. Related keywords may be identified for a given search term using a combination of an aggregated life-stream analysis, a user profile based analysis, and/or a community based life-stream analysis. Each of these mechanisms identify a subset of updated life-streams or profiles to be analyzed to identify keywords that co-occur within each life-steam along with the given search term. The resulting co-occurring keywords may then be ordered based on a frequency of occurrence for the subset of life-streams. A weighted combination of the identified related keywords may be used to display related keywords to a user for use in modifying a search query.