Time-variant Markov Model for User Behavior Prediction
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Solution Overview
Problem
Service providers face challenges in determining user interests and identifying non-human entities, as user behavior changes over time and existing user profiles fail to reflect these changes, leading to irrelevant content delivery and difficulty in identifying malicious bots.
Innovation Solution
A time-variant variable-order model, such as a Markov model, is generated using webpage navigation data to predict user interactions and identify non-human entities by transforming navigation patterns into a context tree with substring operators, allowing for the pruning of irrelevant branches and providing probabilities for content delivery and server capacity adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static user profile is used to determine user interests, then the system is simple to implement, but the profile cannot reflect changing user behavior over time
Solution Approach 1:
The patent transforms static user profiles into dynamic structures by incorporating time-variant variables that automatically update based on observed user behavior patterns. The profile evolves from a fixed representation to a living model that adapts its parameters (interests, preferences, behavior patterns) as users interact with the system over time, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with content are continuously monitored and fed back into the profile update process. This closed-loop approach allows the profile to learn from actual user behavior patterns and adjust its representations accordingly, enabling the profile to reflect changing interests without requiring manual intervention.
2Reliability
If content is delivered based on outdated user profiles, then the content delivery system is simple, but user satisfaction decreases due to irrelevant content
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and updates profiles in advance before content delivery decisions are made. By proactively learning and adapting user preferences ahead of time, the system ensures that content recommendations are based on the most current user interests, improving relevance without adding significant complexity to the delivery process.
Solution Approach 2:
The patent changes the parameters used in content delivery decisions from static profile attributes to dynamic, time-variant parameters that reflect current user behavior. This transformation allows the system to maintain high content relevance by continuously updating the behavioral parameters that drive recommendation algorithms, balancing reliability with acceptable system complexity.
3Measurement precision
If traditional methods are used to identify non-human entities, then the detection system is simple, but the ability to identify malicious bots is insufficient
Solution Approach 1:
The patent introduces an intermediary analysis layer that examines user behavior patterns as a mediator between raw interaction data and bot detection decisions. This intermediate layer processes and analyzes behavioral sequences, timing patterns, and interaction characteristics to identify anomalies that indicate non-human entities, improving detection accuracy while keeping the overall system architecture manageable through modular design.
Solution Approach 2:
The system adds another dimension to bot detection by analyzing temporal patterns and behavioral sequences rather than relying solely on traditional single-point-in-time detection methods. By examining user interactions across multiple time dimensions and behavioral contexts, the system achieves higher detection accuracy for malicious bots while maintaining system simplicity through efficient pattern recognition algorithms.
Data Source
AI summary
A webpage navigation of a user over a timeframe and a second webpage navigation of a second user over a second timeframe may be received. A time-variant variable-order Markov model, comprising a context tree, may be generated utilizing the webpage navigation and the second webpage navigation. A third webpage navigation of a third user may be received. A probability that the third user may interact with content, that the third user is a non-human entity, and/or that the third user will access a website may be determined based upon an evaluation of the third webpage navigation using the time-variant variable-order Markov model. A second client device is instructed to present the content to the third user, to present a human verification mechanism to the third user, and/or to instruct a server, providing the website, to alter a server capacity for the website.