Predictive Search Engine Personalizing Enterprise Results
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Solution Overview
Problem
Current search systems within enterprises fail to provide personalized and efficient results for users due to their inability to consider user attributes, such as past history and preferences, leading to cumbersome manual filtration of irrelevant results.
Innovation Solution
A centralized management system that uses predictive search technology to determine user intent by analyzing keywords, user journey, and historical data, providing relevant knowledge articles and refining search queries to deliver targeted results based on user-specific information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current keyword-based search is used, then search coverage is comprehensive, but search efficiency and user satisfaction deteriorate due to lack of personalization and manual filtration requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing user data including past search history, user profile attributes, and session context before the actual search query is executed. This preparation enables the search algorithm to immediately personalize results without requiring manual user intervention during the search process.
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing user interactions with search results, including clicks, dwell time, and refinement actions. This feedback loop allows the system to learn from user behavior and dynamically adjust future search results to better match user intent and preferences.
2Loss of information
If comprehensive search results are returned, then information coverage is complete, but time to find relevant information increases
Solution Approach 1:
The system applies local quality by tailoring the search results to each user's specific context, attributes, and preferences. Instead of providing uniform results for all users, the system customizes the presentation, ordering, and filtering of results based on individual user characteristics, ensuring that each user receives information most relevant to their needs.
Solution Approach 2:
The system performs preliminary filtering and ranking of search results based on user profile data and historical behavior before presenting results to the user. This pre-processing reduces the effective result set size while maintaining comprehensive information coverage, allowing users to quickly locate relevant information without manually filtering through irrelevant results.
3Ease of operation
If personalized search is implemented, then user satisfaction improves, but system complexity increases
Solution Approach 1:
The system segments the personalization functionality into distinct modular components including user profile management, session context tracking, search algorithm customization, and result ranking mechanisms. This segmentation allows each component to be developed, maintained, and optimized independently, reducing overall system complexity while enabling comprehensive personalization.
Data Source
AI summary
With regard to searches and, more particularly, to searches performed on information repositories belonging to an enterprise, a centralized management system is used by the enterprise to manage the predictive search experience for users. A system offers a rich resolution experience to the end users based on user intent as determined from a variety of mechanisms, such as keywords, end user journey, clustered journey, etc. Also disclosed herein is a system that derives and offers various suggestions to end users to help them accomplish their objectives.