Dynamic Trust Profile for Personalized Recommendation Systems
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Solution Overview
Problem
Conventional recommendation systems are limited in modeling trust, failing to adapt to users' changing trust profiles and requiring users to manually assess trust in decisions, leading to increased time and effort, especially in dynamic contexts like ride-sharing.
Innovation Solution
A trust-based dynamic personalized recommendation system that learns user-specific dynamic trust factors through invasive and non-invasive data collection, creating a dynamic trust profile and providing a trust score based on context-specific factors such as demographics, interactions, and emotions, allowing for automated and intuitive trust assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional heuristic recommendation systems are used, then device complexity is reduced, but trust modeling accuracy deteriorates
Solution Approach 1:
The patent segments trust modeling into multiple dimensions including cognitive trust, affective trust, and situational trust factors. Each dimension is modeled separately with specific variables (e.g., reliability, benevolence, expertise for cognitive trust; emotional bonds for affective trust), allowing comprehensive trust assessment while maintaining manageable system complexity through modular structure
Solution Approach 2:
The system implements dynamic trust profiling that adapts to changing user contexts, preferences, and interactions over time. Trust scores are continuously updated based on new data from invasive and non-invasive sources, enabling the system to capture evolving trust relationships rather than relying on static heuristic rules
2Measurement precision
If manual user assessment of trust is required, then trust evaluation accuracy is improved, but user effort increases
Solution Approach 1:
The system automatically collects and processes trust-related data from multiple sources including user interactions, profile information, and external data feeds. The automated trust scoring mechanism eliminates the need for manual user assessment while maintaining high accuracy through multi-factor analysis and machine learning algorithms
Solution Approach 2:
The system incorporates feedback loops where user responses to recommendations and explicit trust assessments are fed back into the trust model. This continuous feedback mechanism refines trust predictions over time, improving accuracy while keeping the user interface simple and requiring minimal user input
3Adaptability or versatility
If generic unstructured profile data is provided, then system complexity is reduced, but adaptability to changing trust profiles deteriorates
Solution Approach 1:
The system employs dynamic trust profiles that are continuously updated based on changing user behaviors, contexts, and interactions. Trust factors are re-weighted and re-calculated in real-time to reflect current user preferences and situational contexts, enabling high adaptability to evolving trust relationships
Solution Approach 2:
The system changes key parameters of trust modeling including the weighting of different trust dimensions, the threshold for trust-based recommendations, and the data collection intensity based on user context and system state. These parameter adjustments allow the system to adapt to different scenarios while maintaining manageable complexity through controlled optimization
4Measurement precision
If comprehensive data collection is performed, then trust profile accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary data collection and preprocessing during user interactions and idle periods, preparing trust profiles in advance before they are needed for recommendations. This anticipatory approach ensures comprehensive data is already processed and ready, eliminating delays when trust assessments are required
Solution Approach 2:
The system continuously collects and processes trust-related data in the background without interrupting user workflows. Data collection from invasive and non-invasive sources operates continuously, and trust scores are updated in real-time, ensuring comprehensive accuracy while maintaining seamless user experience without noticeable processing delays
Data Source
AI summary
Methods and systems for personalized trust recommendation based on a dynamic trust model. In an example embodiment, a trust framework can be derived from a set of use-case specific factors. Invasive data and non-invasive data can be collected from a user (or a group of users) based on activity data and profile data associated with the user. A dynamic trust profile can be created (or learned) based on the invasive data and the non-invasive data collected from the user. A recommended level of trustworthiness can be then provided to the user respect to a particular situation and/or entity (e.g. other users) within the trust framework based on the dynamic trust profile of the user and which is personalized for the user.