Contextual People Search via Content Graph Filtering
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Solution Overview
Problem
Existing people search technologies fail to provide contextual and real-time results, often returning irrelevant individuals due to their focus on explicit profile information rather than relevant content, making it difficult to find skilled or interested individuals within large datasets.
Innovation Solution
A method involving a computing device that generates a search graph from a directional graph representing people and content, filtering out irrelevant content and adding relevance measures, to identify sources of relevant content matching user queries, thereby personalizing and contextualizing search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional search approach is used that focuses on explicit profile information, then the search can be performed quickly, but the search results are not contextualized and may return irrelevant individuals
Solution Approach 1:
The system pre-generates a comprehensive graph data structure that maps relationships between individuals and content topics before any search query is received. This preliminary action stores the organizational knowledge graph with all connections between people and their associated content, enabling rapid querying without real-time computation of relationships.
Solution Approach 2:
The patent introduces a content-based intermediary layer (the graph data structure) that mediates between the search query and the individuals being searched for. Instead of directly searching profile information, the system uses the graph as an intermediary to find individuals through their content relationships, thereby improving contextual relevance while maintaining speed.
2Measurement precision
If a comprehensive search across all individuals and content is performed to ensure contextual relevance, then the search results are more accurate, but the search cannot be completed in real time
Solution Approach 1:
The system performs the computationally intensive work of mapping all relationships between individuals and content in advance, storing the results in a pre-computed graph data structure. When a search query arrives, the system only needs to traverse the pre-established relationships in the graph, which is much faster than performing comprehensive analysis in real time.
Solution Approach 2:
The graph data structure is dynamically generated based on the specific search query parameters while still leveraging pre-computed relationships. The system adapts the comprehensive graph to the specific search context by filtering and traversing relationships relevant to the query terms, balancing comprehensiveness with real-time performance.
3Measurement precision
If the search focuses on explicit profile information, then the search process is simple and fast, but it fails to identify individuals based on their actual expertise and content contributions
Solution Approach 1:
The patent introduces a content-based intermediary layer (the graph data structure) that mediates between the search query and the individuals being searched for. Instead of directly searching profile information, the system uses the graph as an intermediary to find individuals through their content relationships, thereby improving contextual relevance while maintaining speed.
Solution Approach 2:
The system extracts the essential relationship information from the comprehensive organizational data and stores it in a simplified graph data structure that contains only the relevant connectivity information needed for searching. This extraction separates the search functionality from the detailed profile data, improving both accuracy and efficiency.
Data Source
AI summary
A search query relating to a search for people and including one or more search terms is received. A search graph for the search is created from a previously created graph which is a directional graph in which people are represented by nodes and content is represented by nodes or edges. The search graph is generated by filtering out nodes or edges that represent content that is determined not to be relevant to the search terms in the search query; and, for the remaining content that is determined to be relevant to the search terms in the search query, adding to the graph a measure of the relevance of the content to the search terms. The search results are generated by operating on the search graph to identify people in the search graph who are sources of content that is relevant to the search terms.