Nutritional Preference Prediction for Personalized Online Interfaces
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Solution Overview
Problem
Online systems lack flexibility and personalization to identify and surface users' specific nutritional needs and preferences at a large scale without self-reporting or manual processes.
Innovation Solution
A machine-learning model is trained to predict user preferences for nutritional attributes based on user interactions, applying a vector of scores to generate a user interface highlighting relevant nutritional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual self-reporting processes are used to identify user nutritional preferences, then accuracy of preference identification is improved, but system scalability and ease of operation deteriorate
Solution Approach 1:
The system automatically infers nutritional preferences by analyzing user interaction patterns with nutritional information, eliminating the need for users to manually self-report. The machine learning model processes user behavior data (views, clicks, searches) to automatically determine preferences, enabling both high accuracy and large-scale operation simultaneously
Solution Approach 2:
The patent replaces manual self-reporting mechanisms with an automated machine learning system that uses computational algorithms to infer preferences from digital interaction data. This substitution eliminates the scalability limitations of manual processes while maintaining preference identification accuracy through sophisticated pattern recognition
2Reliability
If manual processes are used to identify user nutritional preferences, then reliability of preference data is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system performs self-service by automatically analyzing user interaction patterns to infer nutritional preferences without requiring user input. The machine learning model processes behavioral data (views, clicks, searches) to reliably determine preferences, reducing system complexity from manual data collection to automated inference
3Productivity
If automated preference identification is implemented, then ease of operation and scalability are improved, but measurement precision of nutritional preferences deteriorates
Solution Approach 1:
The system uses feedback loops where user interactions with nutritional information (views, clicks, searches) continuously feed into the machine learning model to refine preference predictions. This feedback mechanism enables automated scaling while improving measurement precision through iterative learning from actual user behavior patterns
Solution Approach 2:
The patent employs machine learning algorithms that substitute manual processes with intelligent automated systems. The model processes complex behavioral patterns to accurately infer nutritional preferences at scale, achieving both high scalability and precise measurement through computational intelligence
Data Source
AI summary
A trained model is used to generate a user interface of an online system based on predicted nutritional preferences for a user of the online system. Upon receiving a signal indicating interaction of the user with the online system, the online system applies the trained model to output, based on user's features, item features and/or session features, a vector of scores for the user, where each score is indicative of a preference of the user for a respective nutritional attribute of a set of nutritional attributes. Responsive to a score being greater than a threshold score, the online system generates, based on the received signal, a user interface of a device associated with the user that includes a label about the nutritional attribute associated with the score. The online system causes the device associated with the user to display the user interface with the label about the nutritional attribute.