Propensity Model Selection for Recommendation Engines
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Solution Overview
Problem
Building recommendation tools from scratch is an expensive and time-consuming process, especially when deciding on data types, quality, and formats, and choosing between different models, which can be complex and resource-intensive.
Innovation Solution
A model training system that selects the best propensity or collaborative filtering model based on accuracy and repeatability thresholds, preprocessing data to ensure it meets modeling criteria, and transforming data to standard formats for efficient model training and recommendation generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation tools are built from scratch using traditional methods, then model accuracy can be achieved, but the process is expensive and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically preprocessing data (cleaning, transforming, validating) and evaluating multiple propensity models before final selection. This automated preliminary work eliminates manual data preparation steps that traditionally consumed significant time and resources, while ensuring data quality meets modeling requirements.
Solution Approach 2:
The system implements self-service by autonomously selecting the appropriate propensity model based on data characteristics and performance metrics. The automated model selection process evaluates multiple candidates and chooses the best fit without requiring expert intervention, reducing both time and cost while maintaining accuracy.
2Reliability
If multiple propensity models are trained and evaluated to ensure accuracy, then model selection quality improves, but computational resources and time increase
Solution Approach 1:
The system segments the model evaluation process by dividing data into training and testing sets, and by evaluating models in stages (first accuracy on training data, then second accuracy on testing data). This segmented approach allows efficient resource utilization while maintaining rigorous evaluation standards for reliable model selection.
Solution Approach 2:
The system changes parameters by adjusting the number and type of propensity models evaluated based on data characteristics and requirements. The automated system can dynamically modify evaluation parameters such as accuracy thresholds and model complexity to balance resource consumption with selection quality.
3Productivity
If data is preprocessed and transformed to meet modeling criteria, then model training efficiency improves, but data processing complexity increases
Solution Approach 1:
The system implements universal data preprocessing functions that handle multiple data types and formats through standardized processes. The automated preprocessing pipeline performs cleaning, transformation, and validation operations that work across different data sources, reducing the need for custom processing logic while improving training efficiency.
Solution Approach 2:
The system replaces manual mechanical data processing with automated computational processes. Algorithms automatically perform data cleaning, transformation, and validation tasks that previously required manual intervention, reducing processing complexity while enhancing efficiency through systematic automated operations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a recommendation engine. One of the methods includes receiving, from a plurality of data sources, input data; generating, using each of two or more propensity models, output data by providing training data from the input data to the respective propensity model; determining, for each of the propensity models, a first accuracy of the respective propensity model using the respective output data; determining, for each of the two or more propensity models, a second accuracy of the respective propensity model using testing data from the input data; selecting, using the first accuracies and the second accuracies for the two or more propensity models, a propensity model from the two or more propensity models; and providing, to a system, the selected propensity model to enable the system to generate a recommendation using the selected propensity model.