ML Model for User Elasticity and Recommendations
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Solution Overview
Problem
Conventional AI-backed recommendation methods are inefficient in processing heterogeneous data from disparate sources, leading to unreliable predictions and recommendations due to high processing power requirements and subjective expert interpretation.
Innovation Solution
A processor-based system generates elasticity scores and uplift scores for users by analyzing heterogeneous data, including transactional, campaign, and behavioral data, using machine learning models and graph convolution networks to provide targeted marketing campaign insights and item recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AI-backed recommendation methods are used to analyze heterogeneous data, then prediction reliability may be improved, but processing power requirements increase significantly
Solution Approach 1:
The patent segments users into distinct groups (winners, losers, stoplosers, stopwinners) based on their response to price changes and marketing campaigns. This segmentation allows the system to process heterogeneous data more efficiently by focusing computational resources on specific user subsets rather than treating all users uniformly, thereby reducing overall processing power requirements while maintaining prediction reliability.
Solution Approach 2:
The patent transforms heterogeneous data from multiple sources into standardized elasticity scores and uplift scores through parameter transformations. By converting diverse data formats into unified numerical parameters, the system reduces processing complexity and energy consumption while improving the reliability of predictions through more consistent and comparable data representations.
2Measurement precision
If heterogeneous data from disparate sources is processed, then recommendation accuracy may be improved, but processing efficiency decreases
Solution Approach 1:
The patent introduces elasticity scores and uplift scores as intermediary metrics that mediate between heterogeneous data sources and final recommendations. These intermediary parameters serve as a common language that translates diverse data formats into unified representations, enabling accurate recommendations while improving processing efficiency by avoiding the need to directly process and reconcile all heterogeneous data sources simultaneously.
Solution Approach 2:
The patent extracts key insights from heterogeneous data by identifying and isolating specific patterns (winners, losers, stoplosers, stopwinners) that drive recommendation accuracy. By extracting only the most relevant patterns rather than processing all data uniformly, the system maintains high recommendation accuracy while significantly improving processing efficiency.
3Extent of automation
If expert interpretation is used to analyze user data, then subjectivity may be reduced, but reliability of predictions decreases
Solution Approach 1:
The patent implements self-service through automated machine learning models that independently analyze user data and generate recommendations without requiring expert intervention. The system automatically segments users, calculates elasticity scores, determines uplift potentials, and generates personalized recommendations, thereby increasing automation while improving reliability through consistent, objective computational processes.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from user responses to marketing campaigns and adjusts its segmentation and scoring models accordingly. This feedback loop allows the automated system to improve its prediction reliability over time by adapting to changing user behaviors and preferences without requiring expert reanalysis.
Data Source
AI summary
A method may include generating a feature table, hierarchical segments, and a graph network based on raw interaction data of a set of users. The method may further include generating a set of rankings for features in the feature table. The method may further include targeting hierarchical segments of the set of users through marketing campaigns and calculate a set of elasticity scores for the set of users in response to the marketing campaigns in the hierarchical segments. The method may further include generating item recommendations for the set of users based on the graph network. The method may further include executing a machine learning model to generate an uplift score for each user from the set of users based on at least one of the raw interaction data, the set of rankings, hierarchical segments, the set of elasticity scores, or the item recommendations.