Personalized Thrill Ride Recommender Using ML
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Solution Overview
Problem
Conventional systems fail to provide personalized thrill ride recommendations at amusement parks, leading to issues such as slip and fall accidents, incorrect seat choices, and inadequate consideration of user health and preferences.
Innovation Solution
A computer-implemented method using a machine learning model trained with ride data, user data, and crowd-sourced historical data to dynamically adjust recommendations for thrill rides, taking into account user health, preferences, and safety considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems provide general thrill ride recommendations without personalization, then device complexity is reduced, but user safety and experience quality deteriorate due to lack of consideration for individual health conditions and preferences
Solution Approach 1:
The system segments users into different groups based on their health conditions, age, and preferences using clustering algorithms. This segmentation allows the system to provide personalized recommendations for each segment while managing complexity through modular data processing and model training components that handle different user groups independently.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw user data and ride recommendations. This intermediary processes and interprets complex user health data, preferences, and historical information, transforming them into safe and personalized ride suggestions without requiring the overall system architecture to become proportionally more complex.
2Measurement precision
If the system collects and processes extensive user data including health information and preferences, then recommendation accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing user data, health information, and preferences in advance before the actual recommendation process. User profiles are pre-built and maintained in the database, allowing the machine learning model to quickly retrieve and process pre-organized data during recommendation generation, reducing real-time processing time.
Solution Approach 2:
The system maintains continuous data collection and model training operations. As new user data and feedback become available, the machine learning model is continuously refined and retrained, ensuring that recommendation accuracy improves over time without requiring complete reprocessing of all historical data, thus optimizing the balance between accuracy and processing time.
3Adaptability or versatility
If the system uses machine learning models with crowd-sourced historical data, then recommendation personalization improves, but system complexity and training requirements worsen
Solution Approach 1:
The patent employs a universal machine learning framework that handles multiple functions: clustering users, generating recommendations, and providing safety assessments. This multi-functional approach allows the system to achieve high personalization and adaptability through a single integrated model architecture, reducing the need for multiple specialized systems and thereby managing overall complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where user responses to recommendations and actual ride experiences are collected and fed back into the machine learning model. This feedback loop enables the model to continuously learn and adapt to individual user preferences and health conditions, improving personalization over time while the automated feedback processing maintains system complexity at manageable levels.
Data Source
AI summary
Embodiments receive ride data, user data which comprises user information and other user data, and crowd-sourced historical data, train a machine learning model using a knowledge corpus which includes the ride data, the user data, and the crowd-sourced historical data, and dynamically adjust at least one ride recommendation based on the trained machine learning model.