Federated Learning Hotel Upsell Model
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Solution Overview
Problem
Hoteliers face challenges in effectively profiling guests and offering personalized pricing and recommendations due to the limitations of traditional one-size-fits-all revenue management policies, especially in the competitive hotel industry.
Innovation Solution
The implementation of a machine learning-based upsell model that generates hierarchical prediction models for different hotel chains, using reservation data to optimize upselling and initial offers, while maintaining data privacy through horizontal federated learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional one-size-fits-all revenue management policies are used, then implementation is simple, but effectiveness in maximizing profit is poor
Solution Approach 1:
The patent segments the homogeneous revenue management approach into personalized, guest-specific strategies. It divides guests into different profiles based on their behavior, preferences, and booking patterns, then applies customized pricing and recommendations to each segment, thereby improving effectiveness while maintaining manageable complexity through systematic categorization
Solution Approach 2:
The patent implements local quality by tailoring revenue management policies to individual guest characteristics rather than applying uniform rules. Each guest receives customized pricing, room recommendations, and promotional offers based on their specific profile, which optimizes profit extraction from each customer segment while adapting to local (individual) needs and preferences
2Productivity
If hierarchical prediction models are generated for different hotel chains using reservation data, then personalized pricing and recommendations improve revenue, but data privacy concerns arise
Solution Approach 1:
The patent introduces a federated learning server as an intermediary that enables collaboration between hotel chains without direct data sharing. The server coordinates the training of hierarchical prediction models by aggregating model parameters from participating chains, allowing personalized revenue management to improve while guest data remains localized and private to each organization
Solution Approach 2:
The patent uses copying by creating and sharing model parameters (copies of knowledge) rather than sharing the actual guest reservation data. Each hotel chain generates prediction models from its own data, then shares the learned parameters with the federated server, which aggregates them into improved models that are distributed back to participants, thereby capturing revenue benefits without exposing sensitive information
3Loss of information
If federated learning is used to average model parameters across hotel chains, then data privacy is maintained, but model training complexity increases
Solution Approach 1:
The patent merges the model training processes of multiple hotel chains into a unified federated learning system. By combining the hierarchical prediction models across chains and averaging their parameters through the federated server, the system achieves improved generalization and performance while maintaining a distributed architecture that manages complexity through coordinated collaboration rather than centralized control
Data Source
AI summary
Embodiments upsell a hotel room selection by generating a first hierarchical prediction model corresponding to a first hotel chain, the first hierarchical prediction model receiving reservation data from one or more corresponding first hotel properties, and generating a second hierarchical prediction model corresponding to a second hotel chain, the second hierarchical prediction model receiving reservation data from one or more corresponding second hotel properties. At each of the first hierarchical prediction model and the second hierarchical prediction model, embodiments generate corresponding model parameters. At a horizontal federated server, embodiments receive the corresponding model parameters and average the model parameters to be used as a new probability distribution, and distribute the new probability distribution to the first hotel properties and the second hotel properties.