Personalized Recommendation System Using ML Inference
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Solution Overview
Problem
Current decision support systems lack the ability to personalize recommendations based on individual customer traits and preferences, often relying on explicit user input or surveys, which can be impractical and prone to errors, especially in complex decision-making scenarios.
Innovation Solution
A personalized interactive decision support system that uses machine learning models to correlate customer traits with consumption preferences and maps these preferences to product attributes, allowing for personalized product recommendations without explicit user preference elicitation, utilizing data from various user-generated content sources like text, audio, and images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit user input or surveys are used to gather preference data, then recommendation accuracy may be improved, but system complexity and user burden increase significantly
Solution Approach 1:
The system automatically infers customer traits and preferences from user-generated content sources without requiring explicit user input. The machine learning model processes available data (text, audio, images) to derive preference information autonomously, eliminating the need for surveys or manual preference elicitation while maintaining personalization capabilities
Solution Approach 2:
The patent replaces the mechanical survey-based preference collection method with an automated machine learning-based inference system. Instead of manually collecting preference data through surveys, the system uses ML models to automatically extract and infer preference information from various user-generated content sources
2Measurement precision
If explicit user input or surveys are used to gather preference data, then recommendation accuracy may be improved, but time consumption and practicality worsen
Solution Approach 1:
The system performs preliminary inference of customer traits and preferences automatically from available user-generated content before the recommendation process begins. By pre-processing and inferring preference data from existing sources (social media, emails, documents), the system eliminates the time-consuming survey administration and manual data collection process
Solution Approach 2:
The system automatically infers customer traits and preferences from user-generated content sources without requiring explicit user input. The machine learning model processes available data (text, audio, images) to derive preference information autonomously, eliminating the need for surveys or manual preference elicitation while maintaining personalization capabilities
3Productivity
If machine learning models infer preferences from user-generated content, then personalization efficiency is improved, but measurement precision of preferences may worsen
Solution Approach 1:
The patent introduces consumption preferences as an intermediary layer between customer traits and product attributes. The machine learning model first infers high-level consumption preferences from customer traits, which then serve as mediators to map to specific product attributes. This two-stage approach improves measurement precision by breaking down the complex inference task into manageable steps with explicit mapping relationships
Solution Approach 2:
The patent replaces the mechanical survey-based preference collection method with an automated machine learning-based inference system. Instead of manually collecting preference data through surveys, the system uses ML models to automatically extract and infer preference information from various user-generated content sources
4Measurement precision
If a comprehensive mapping data structure is created to map product attributes to consumption preferences, then recommendation relevance is improved, but device complexity increases
Solution Approach 1:
The patent segments the preference modeling into distinct components: customer traits, consumption preferences, and product attributes. The comprehensive mapping data structure is divided into multiple relationship mappings (customer-to-preference, preference-to-product) rather than a single complex mapping. This segmentation makes the system more manageable and maintainable while preserving comprehensive coverage
Solution Approach 2:
The patent introduces consumption preferences as an intermediary layer between customer traits and product attributes. The machine learning model first infers high-level consumption preferences from customer traits, which then serve as mediators to map to specific product attributes. This two-stage approach improves measurement precision by breaking down the complex inference task into manageable steps with explicit mapping relationships
Data Source
AI summary
A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a personalized interactive decision support system. A personalized product recommendation module executing within the personalized interactive decision support system correlates at least one customer to a set of consumption preferences using a machine learning model based on a set of traits of the at least one customer to form at least one customer-to-preference correlation. The personalized product recommendation module maps a set of products to the set of consumption preferences using a consumption preferences-to-product attribute mapping data structure based on a set of attributes of the set of products to form a set of product-to-preference correlations. The personalized product recommendation module matches the at least one customer to at least one product within a set of products based on the at least one customer-to-preference correlation and the set of product-to-preference correlations to form at least one product recommendation. A visual and interactive decision support module executing within the personalized interactive decision support system presents the at least one product recommendation.