Two-Model Recommender System for Subconscious and Conscious Decision Modeling
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Solution Overview
Problem
Recommender systems fail to effectively integrate subconscious and conscious user reactions to provide personalized recommendations, as they often rely on single models that do not account for the user's emotional and rational decision-making processes simultaneously.
Innovation Solution
A two-model approach is employed, where one model represents the user's subconscious intuition and another their conscious analytic processes, with a blending function to combine recommendations, allowing for a more comprehensive understanding of user preferences by monitoring both subconscious reactions and conscious decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single model is used to generate recommendations, then the system is simpler to implement, but it fails to capture both subconscious emotional reactions and conscious rational decisions
Solution Approach 1:
The user decision-making process is segmented into two distinct models: a subconscious model that captures emotional reactions and a conscious model that captures rational decisions. Each model processes user interactions separately, allowing the system to capture different aspects of user preferences without requiring a single overly complex model.
Solution Approach 2:
The patent combines the outputs of the subconscious model and conscious model through a blending function that integrates both emotional and rational factors. This merging allows the system to leverage the strengths of both models to generate comprehensive recommendations that account for the full spectrum of user decision-making.
2Loss of information
If only conscious decisions are monitored, then data collection is simpler, but the system misses subconscious emotional reactions that influence user preferences
Solution Approach 1:
The patent introduces an intermediary component that detects subconscious emotional reactions through physiological signals (such as heart rate, skin conductance, or eye tracking) without requiring direct user input. This intermediary bridges the gap between conscious decisions and subconscious emotions, capturing both types of data to provide a complete picture of user preferences.
3Measurement precision
If the system uses multiple models and monitoring mechanisms, then recommendation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system implements partial monitoring by selectively applying subconscious detection to high-value user interactions rather than continuously monitoring all activities. The blending function also selectively weights the outputs of different models based on the specific recommendation context, reducing unnecessary computational overhead while maintaining accuracy where it matters most.
Data Source
AI summary
A process for recommending choices to a user uses a first model representing the user's subconscious and a second model representing the user's consciousness. The process comprises receiving data and performing, using the second model, a simulation by modeling the user and a third party to derive a simulation result that determines how the user would react to a situation based at least in part on the received data. Using the first model and the simulation, a first set of recommendation choices based at least in part on the received data is derived, and a set of recommendation choices for presentation based on the first set of recommendation choices is determined and presented to the user. As the user is making a selection, the user is monitored for conscious decisions and subconscious reactions, which are used to update the first model and the second model.