User State Model Segmentation for Silent User Detection
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Solution Overview
Problem
Conventional social network models fail to accurately predict the final state of users regarding events, as they only consider activated and non-activated states, neglecting the silent state and various social psychology factors, leading to incomplete monitoring of user states.
Innovation Solution
A novel user state model that includes activated, non-activated, and unstable silent states, using user-event similarity to identify silent or non-activated users, and determining their final state through iterative processes and thresholds, considering factors like associated users' states and event similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional two-state models (activated/non-activated) are used, then the model complexity is low, but the user state prediction accuracy is insufficient
Solution Approach 1:
The patent segments the conventional two-state model into three distinct states: activated state, non-activated state, and silent state. This segmentation allows for more precise characterization of user behavior by distinguishing between users who are actively engaged, completely uninterested, and those who are in an intermediate uncertain state, thereby improving prediction accuracy without excessive complexity increase
Solution Approach 2:
The patent adds a temporal dimension to the state model by introducing time delay parameters and state transition probabilities that evolve over time. This dimensional expansion captures the dynamic nature of user engagement, allowing the model to predict not just the final state but also the timing and progression of state changes, enhancing predictive capability
2Adaptability or versatility
If more user factors and social psychology phenomena are considered, then the comprehensiveness of user state monitoring improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary classification of users into the three states at the beginning of the analysis, which establishes a foundation for subsequent predictions. By pre-defining state categories and transition rules, the system reduces the complexity of real-time analysis while maintaining comprehensive monitoring capabilities across multiple user factors and social psychology phenomena
Solution Approach 2:
The model incorporates feedback mechanisms where the states of associated users influence the target user's state transitions. This feedback loop captures social psychology effects such as herd behavior and peer influence, allowing comprehensive monitoring of social network dynamics while using probabilistic transition rules to manage computational complexity
3Reliability
If the silent state is neglected, then the model simplicity is maintained, but the completeness of user state characterization deteriorates
Solution Approach 1:
The patent explicitly segments the user population into three categories including the silent state, which represents users who are neither actively engaged nor completely disengaged. This segmentation captures the nuanced reality of social network behavior where many users remain in an intermediate state, thereby improving the reliability and completeness of user state characterization
Solution Approach 2:
The model uses parameter changes to represent transitions between states, including the silent state. By defining specific probability parameters for entering and exiting the silent state, the model accurately characterizes user behavior dynamics while maintaining a manageable structural complexity through standardized transition mechanisms
Data Source
AI summary
A method and an apparatus for identifying a state of a user of a social network. The identification method includes acquiring a user-event similarity of a user regarding a new event; identifying whether the user is a silent user or a non-activated user according to the user-event similarity; and determining whether the silent user or the non-activated user on the social network is finally in an activated state or a non-activated state. In the foregoing manner, a novel user state model of a social network is designed in the present disclosure, the model includes an activated state, a non-activated state and an unstable silent state, and a final state of a user is inferred precisely under full and comprehensive consideration of factors that may affect the state of the user, such that the state of the user can be accurately and precisely monitored.