Acyclic Graph Analysis for Website User Retention Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems managing website display struggle to efficiently retain users due to ineffective content presentation, leading to reduced performance and increased resource wastage, as manual analysis of historical browsing data is impractical for predicting user actions and optimizing operational goals.
Innovation Solution
A data processing system utilizes machine-learning algorithms to train inference models from historical browsing data, generating acyclic graphs that analyze user and operator actions to identify patterns and predict user behavior, thereby optimizing content presentation and resource allocation to improve user retention and operational goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of historical browsing data is used to predict user actions, then user retention can be improved, but the process becomes impractical and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis of browsing data with automated machine-learning algorithms and inference models. The system automatically trains models on historical data, generates acyclic graphs representing user action sequences, and predicts future user actions without human intervention, thereby maintaining reliability while dramatically improving productivity.
Solution Approach 2:
The system enables self-service by allowing the machine-learning models to automatically learn from historical browsing data, generate predictions, and optimize content presentation without requiring manual analysis. The inference models continuously improve through automated training on new data, making the system self-sufficient and highly efficient.
2Reliability
If content presentation is optimized to maximize user engagement, then user retention increases, but system complexity increases
Solution Approach 1:
The patent segments the complex task of user retention optimization into distinct components: training separate inference models for different user actions, generating acyclic graphs that break down user behavior sequences, and analyzing specific traversal paths through the graphs. This modular segmentation manages complexity while maintaining effective user engagement optimization.
Solution Approach 2:
The patent introduces acyclic graphs as an intermediary representation between raw browsing data and content presentation decisions. These graphs serve as a structured medium that captures user action sequences and relationships, enabling the system to analyze and optimize content presentation without directly managing the full complexity of raw user behavior data.
3Productivity
If machine-learning algorithms are used to analyze user behavior patterns, then resource efficiency improves, but computational resources are consumed
Solution Approach 1:
The patent performs preliminary action by training inference models on historical browsing data in advance, before actual user interactions occur. The system pre-generates acyclic graphs and pre-analyzes user action patterns, so that during real-time operations, predictions can be made quickly with minimal computational overhead, improving resource efficiency during deployment.
Solution Approach 2:
The patent applies partial action by focusing computational resources on analyzing only the most relevant traversal paths through the acyclic graphs that are most likely to influence user retention. Rather than exhaustively analyzing all possible user action sequences, the system identifies and prioritizes key paths, reducing computational consumption while maintaining effectiveness.
Data Source
AI summary
Methods and systems for managing display of a website to a user are disclosed. Data processing systems operating the website may perform operator actions that increase the likelihood of achieving operational goals, such as presenting relevant content to the user to increase view time and/or user retention. To identify appropriate operator actions, logs (e.g., historical browsing data) may be obtained for various users of the website. The logs may record user actions, operator actions, and/or other information that describes historical and/or current user browsing activity. Inference models may be implemented to predict probable user actions, and associated user action information based on the log information. The predictions may be presented as an acyclic graph that associates the operator actions, user actions, and user action information. The graph may be analyzed to identify operator actions that may maximize user retention and/or increase the likelihood of meeting other operational goals.