Hyper-segmented Personalization via Federated Ensemble Models
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Solution Overview
Problem
Existing e-commerce personalization approaches are centric and monolithic, focusing solely on customer purchase experiences and failing to account for product company perspectives, leading to ineffective customer experience enhancement and product promotion.
Innovation Solution
The implementation of hyper-segmented personalization using a federated ensemble-based machine learning algorithm, which combines purchase experience and product experience models to generate a personalized model that adapts e-commerce site interfaces for individual users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a monolithic personalization approach focusing solely on purchase experiences is used, then the system is simple to implement, but the customer experience enhancement is ineffective
Solution Approach 1:
The patent divides the monolithic personalization model into multiple specialized models: purchase experience models and product experience models. Each model focuses on specific aspects of user interaction, allowing for more targeted and effective personalization while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent combines multiple specialized models (purchase experience models and product experience models) into an ensemble framework that integrates their outputs. This merging allows the system to leverage diverse data sources and perspectives to generate comprehensive personalization recommendations
2Adaptability or versatility
If multiple specialized models are used to capture product company perspectives, then the personalization effectiveness is improved, but the system complexity increases
Solution Approach 1:
The ensemble framework serves multiple functions: it aggregates predictions from purchase experience models, integrates product experience models, and generates unified personalization recommendations. This multi-functionality allows the system to handle diverse data sources and objectives within a single coherent architecture
Solution Approach 2:
The patent introduces an intermediary ensemble mechanism that mediates between the specialized purchase experience models and product experience models. This intermediary layer harmonizes the outputs of different models, resolving conflicts and integrating diverse perspectives into coherent personalization recommendations
3Measurement precision
If federated ensemble learning is applied to combine multiple models, then the personalization accuracy is enhanced, but the computational complexity increases
Solution Approach 1:
The patent applies partial federated ensemble learning by selectively combining models based on their relevance to specific personalization tasks. Rather than always using all available models, the system dynamically selects and combines only the necessary models, reducing computational overhead while maintaining accuracy
Data Source
AI summary
Techniques are disclosed for hyper-segmented personalization using machine learning-based models in an information processing system. For example, a method obtains one or more product experience recommendation data sets respectively from one or more product entities, and one or more purchase experience recommendation data sets respectively from one or more commerce entities. The method applies a federated ensemble-based machine learning algorithm to at least one of the one or more purchase experience recommendation data sets and at least one of the one or more product experience recommendation data sets to generate a personalized model, and causes adaptation of a purchasing interface of at least one of the one or more commerce entities with respect to a given user based on the personalized model.