Access Configuration Ranking Using User History and Benefit Metrics
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Solution Overview
Problem
Conventional systems fail to account for user engagement and system benefit when providing access configurations for digital resources, often leading to inefficient resource utilization and potential waste.
Innovation Solution
A system that utilizes a user's resource access history and machine learning to generate probabilities and benefit metrics for access configurations, prioritizing options that are likely to engage users while optimizing system efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems display all available access configurations to users, then users have complete information to make decisions, but the system cannot identify which configurations users will actually select, leading to potential resource waste
Solution Approach 1:
The system performs preliminary analysis of user access history and behavior patterns before presenting access configurations. By pre-calculating which configurations a user is most likely to select based on historical data, the system avoids presenting all possible configurations and instead focuses on the most probable choices, thereby reducing computational waste while maintaining information quality.
Solution Approach 2:
The system implements a feedback mechanism that continuously learns from user selections and access patterns. By analyzing actual user behavior against predicted behavior, the system refines its probability models for configuration selection, improving its ability to predict user choices and reduce resource waste over time.
2Ease of operation
If the system prioritizes access configurations based on predicted user selection probability, then user engagement improves, but the system complexity increases due to machine learning model integration
Solution Approach 1:
The system introduces a machine learning model as an intermediary component that bridges user behavior analysis and configuration recommendation. This intermediary layer processes raw access history data and transforms it into actionable probability predictions, allowing the rest of the system to operate with simpler logic while maintaining high user engagement through accurate predictions.
Solution Approach 2:
The machine learning model operates autonomously to generate configuration recommendations without requiring manual intervention or complex rule-based systems. The model self-adjusts its parameters based on incoming data, automatically improving its predictions while reducing the operational complexity for system administrators.
3Productivity
If the system presents access configurations ranked by system benefit metrics, then overall system efficiency improves, but individual user preferences may be overlooked
Solution Approach 1:
The system merges two ranking criteria into a unified recommendation approach: user selection probability and system benefit metric. By combining these factors, the system identifies configurations that both the user is likely to accept and that provide maximum benefit to the system, achieving a balance between user preference accommodation and overall efficiency improvement.
Solution Approach 2:
The system dynamically adjusts the weighting between user preference probability and system benefit metrics based on contextual factors. When user engagement is prioritized, the probability factor receives higher weight; when system efficiency is the primary goal, the benefit metric receives higher weight, allowing flexible adaptation to different operational scenarios.
Data Source
AI summary
Systems and methods for ranking access configurations for requested resources. In some aspects, a system receives a resource access request from a user. The system obtains a resource access history for the user. The system generates access configurations available to the user. The system generates a plurality of probabilities using a machine learning model. The system, based on comparing probabilities with a threshold probability, determines a subset of access configurations. The system generates an access benefit metric for each access configuration. The system generates a representation of access configurations according to access benefit metrics.