Knapsack-Based Recommendation Engine for Support Tool Selection
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Solution Overview
Problem
As the number of customers and products increases, the complexity of support incidents grows, leading to a backlog of support requests that can overwhelm limited support staff. Existing automated support systems are resource-intensive and often deployed based on familiarity and experience rather than data-driven insights.
Innovation Solution
A knapsack-based recommendation engine is developed to cluster support requests based on structured and unstructured data, using a machine-learning model to identify patterns and recommend automated support tools. The system tunes hyperparameters iteratively and employs a knapsack optimization algorithm to select tools that maximize resource savings while aligning with available resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated support systems are deployed to reduce support request backlog, then support efficiency is improved, but resource consumption increases
Solution Approach 1:
The system changes the parameters of support tool selection by using data-driven insights from machine learning models and knapsack optimization algorithms. Instead of deploying tools based on manager familiarity and experience, the system analyzes support request data to determine the optimal combination of tools that maximizes support efficiency while constraining resource consumption. This parameter change enables evidence-based decision-making about which automated support systems to deploy.
2Productivity
If more automated support tools are deployed to handle support requests, then support capacity increases, but implementation complexity increases
Solution Approach 1:
The system segments the support tool selection process into distinct components: machine learning model training, hyperparameter tuning, knapsack optimization, and tool recommendation. This segmentation allows each component to be developed, tested, and optimized independently. The knapsack algorithm itself segments the selection process by considering individual tools with their respective resource requirements and effectiveness, enabling systematic selection without overwhelming complexity.
Solution Approach 2:
The knapsack optimization algorithm acts as an intermediary between the available support tools and the resource constraints. It takes as input the list of potential tools, their resource requirements, and their expected effectiveness, then produces an optimized selection that balances capacity expansion with implementation complexity. This intermediary layer simplifies the decision-making process by automatically handling the complexity of tool selection.
3Loss of time
If support tools are selected based on manager experience and familiarity, then deployment speed is improved, but selection accuracy decreases
Solution Approach 1:
The system implements feedback loops where machine learning models continuously analyze support request data to refine tool recommendations. The knapsack optimization algorithm uses feedback from support request outcomes to adjust future selections. This feedback mechanism ensures that selection accuracy improves over time while maintaining deployment speed, as the system learns from past decisions and outcomes.
Solution Approach 2:
The system replaces the mechanical process of manager-based selection (relying on human experience and familiarity) with an automated data-driven system using machine learning and optimization algorithms. This substitution eliminates the bias and limitations of human judgment while maintaining rapid deployment capability through automated tool selection and recommendation.
Data Source
AI summary
A machine-learning model is trained to cluster support requests based on the contents of the support requests. A user of the recommendation system may select a set of support requests to be clustered. Based on the selected set of support requests, the trained machine-learning model may be tuned and used to cluster the selected set of support requests. Using the characteristics of the support requests in one or more generated insights, one or more tools suitable for providing automated support for the cluster of support requests may be identified. Using a knapsack-based approach, one or more of the identified tools is selected for recommendation to the user.