Search Engine Interaction Graph for Content Relevance
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Solution Overview
Problem
Intranet search engines often provide low-relevant content suggestions due to lack of consideration for user interactions within the enterprise, leading to time-consuming searches and reduced productivity.
Innovation Solution
Implementing a search engine that utilizes a member's 'footprint' within the enterprise, represented as an interaction graph, to suggest content items based on historical interactions, organizational position, and expertise, dynamically adjusting the level of indirectness to improve relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines provide content suggestions based on keywords and prior search queries, then search functionality is maintained, but the relevance of suggested content items deteriorates because user interactions and departmental context are not considered
Solution Approach 1:
The search system segments user interactions into distinct categories (direct interactions, indirect interactions through colleagues, organizational position, expertise) and processes each segment separately before integrating results. This allows the system to handle complex user context without overwhelming complexity in a single processing step.
Solution Approach 2:
The patent introduces an intermediary layer that maps user profile attributes (department, expertise, indirect interactions) to content items. This intermediary mapping layer enables the system to translate abstract user characteristics into concrete content recommendations without requiring direct analysis of all possible user-content relationships.
2Productivity
If search engines ignore user interactions and provide suggestions based on keywords, then search operations remain simple, but time to locate relevant documents increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-building user profiles that capture interaction history, organizational position, and expertise before the actual search occurs. This pre-processing of user context information enables the search engine to immediately provide personalized suggestions without requiring real-time analysis of user behavior patterns during the search process.
Solution Approach 2:
The search system incorporates feedback mechanisms where user interactions with content items (viewing, accessing, sharing) are continuously monitored and used to update user profiles. This feedback loop enables the system to progressively improve the accuracy of content suggestions over time, reducing the need for users to manually search through irrelevant results.
Data Source
AI summary
Computer systems, devices, and associated methods of providing personalized content suggestion are disclosed herein. In one embodiment, a method performed by a search engine includes receiving an indication to perform a search for content items from a member. In response to the received indication, the search engine generates a list of content items represented as nodes in an interaction graph. The nodes uniquely correspond to the member from whom the indication to perform the search is received. In the nodes, at least one is indirectly connected to a node representing the member via at least one other node in the interaction graph.