Abnormal User Activity Classification With Trajectory Prediction
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Solution Overview
Problem
Conventional mechanisms for identifying abnormal user activities on interactive platforms often result in false identifications and fail to address self-moderating users, lack interpretability, and are ineffective for users who do not cooperate with intervention measures.
Innovation Solution
A system utilizing a deep-learning model to analyze platform interaction data, classify users into sustained and instantaneous abnormal users, predict abnormality trajectories, and identify extremely abnormal users through a deep Markov model and conditional networks, with latent encoding and SHAP models for explainability and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rule-based models and intelligent models are used to identify abnormal user activities, then abnormal activities can be detected, but false identification of self-moderating users occurs and interpretability is lacking
Solution Approach 1:
The patent segments abnormal users into two distinct categories: sustained abnormal users and instantaneous abnormal users. This segmentation is based on analyzing user behavior patterns over time, where sustained abnormal users exhibit continuous abnormal behavior while instantaneous abnormal users show isolated abnormal incidents. By dividing the detection task into these segments, the system avoids falsely identifying self-moderating users who may have single abnormal incidents but return to normal behavior patterns.
Solution Approach 2:
The patent implements preliminary classification of users into sustained and instantaneous abnormal categories before applying intervention measures. This preliminary action allows the system to differentiate between users who require immediate intervention and those who are self-moderating, thereby reducing false identifications and improving the reliability of abnormal activity detection.
2Speed
If conventional models identify abnormal users based on single instances, then immediate abnormal activities are detected, but self-moderating users are falsely identified
Solution Approach 1:
The patent applies dynamic analysis by examining user behavior patterns over time rather than static single-instance detection. The system continuously monitors user activities and classifies users based on their behavior trajectories, allowing it to distinguish between sustained abnormal behavior and transient incidents. This dynamic approach maintains detection speed while significantly reducing false identification of self-moderating users.
Solution Approach 2:
The system performs preliminary classification of users into sustained and instantaneous abnormal categories before final identification. This preliminary action enables the system to quickly filter out self-moderating users who exhibit only transient abnormal behavior, thereby maintaining high detection speed while improving reliability by reducing false positives.
3Productivity
If intervention measures are applied to all identified abnormal users, then more users are addressed, but users who do not cooperate remain unaffected and resource loss continues
Solution Approach 1:
The patent applies local quality by providing different types of interventions tailored to specific user categories. Sustained abnormal users receive comprehensive intervention measures including account restrictions and counseling, while instantaneous abnormal users receive lighter interventions such as warnings and educational content. This localized approach to intervention quality ensures that resources are effectively allocated and that users receive appropriate measures based on their specific behavior patterns, thereby improving overall intervention effectiveness.
4Adaptability or versatility
If conventional mechanisms lack interpretability, then complex models can be used, but understanding of when and why intervention is needed is lost
Solution Approach 1:
The patent implements feedback mechanisms that provide interpretable information about why users are classified as sustained or instantaneous abnormal. The system generates explanations based on analyzed behavior patterns, such as frequency of abnormal activities, duration of abnormal behavior, and deviation from normal patterns. This feedback loop maintains model flexibility while preventing loss of interpretability by continuously providing actionable insights to operators about intervention needs.
Data Source
AI summary
A system (100) to prevent abnormal user activities associated with interactive platforms. The system (100) includes an abnormality analyzer engine (106) to receive data related to abnormal activities associated with the platform interactions of pre-identified abnormal users and to estimate parameters indicative of extent of abnormality associated with each of the pre-identified users based on the received data. The system also includes a user classification module (108) to classify the pre-identified abnormal users into sustained abnormal users instantaneous abnormal users based on the estimated parameters. The system also includes an abnormality predictor (110) to predict trajectory of abnormality of each of the sustained abnormal users based on the received data of the classified sustained abnormal users and to identify extremely abnormal users based on the predicted trajectories for taking actions to prevent abnormal user activities of the identified extremely abnormal users.