Resource Scoring via Organizational Proximity Datasets
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Solution Overview
Problem
The increasing volume of resources in repositories, such as business intelligence resources, leads to compute resource-intensive recommendation systems, resulting in diminished data precision and negatively impacting organizational decision-making quality due to reliance on outdated or inaccurate information.
Innovation Solution
A resource recommendation system that generates user-targeted recommendations using organizational proximity datasets, including degrees of separation, geographic locations, tenure, and security clearance, to score resources and provide accurate, compute-efficient recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation systems evaluate voluminous repositories of resources to select resources for users, then the variety and quality of recommended resources improve, but compute resource consumption increases
Solution Approach 1:
The system pre-computes organizational network analyses including degrees of separation, geographic locations, tenure information, and security clearance status for all users before recommendation generation. These pre-computed organizational proximity datasets are stored and reused during recommendation transactions, eliminating the need to re-evaluate entire resource repositories for each user query, thus reducing compute resource consumption while maintaining recommendation quality
Solution Approach 2:
The recommendation system segments the evaluation process into two distinct phases: (1) a batch pre-computation phase that analyzes organizational network structures and user profiles, and (2) a lightweight inference phase that generates recommendations using pre-computed data. This segmentation allows heavy computational work to be performed independently of user requests, reducing real-time compute resource consumption
2Measurement precision
If end users spend more time and compute resources identifying suitable resources, then recommendation accuracy improves, but user time expenditure and transaction costs increase
Solution Approach 1:
The system automatically generates personalized resource recommendations by leveraging pre-computed organizational proximity datasets and user behavioral patterns without requiring users to manually search or evaluate resources. The recommendation engine self-serves by matching users with relevant resources based on organizational relationships and resource quality metrics, eliminating time expenditure while maintaining high recommendation accuracy
3Use of energy by moving object
If users rely on previously used resources or peer recommendations to reduce compute expenditure, then compute resource consumption decreases, but data precision and decision-making quality diminish
Solution Approach 1:
The system incorporates resource quality metrics and user behavioral data as feedback signals to continuously refine recommendations. By monitoring resource consumption patterns, usage effectiveness, and organizational network changes, the system dynamically adjusts recommendations to maintain data precision without requiring users to expend compute resources on manual evaluation or reliance on outdated peer recommendations
Data Source
AI summary
A computing device includes a memory and processing circuitry. The memory is configured to store an organizational proximity dataset for a current user. The processing is configured to generate scores for a plurality of resources based on the organizational proximity dataset stored to the memory for the current user. The processing circuitry is further configured to recommend one or more resources of the plurality of resources to the current user based on the scores generated for the plurality of resources.