Analytics Subsystem for User Interest Cloud Generation
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Solution Overview
Problem
Existing content platforms lack efficient methods to analyze user interaction data, which limits their ability to understand user preferences and content relevance effectively.
Innovation Solution
A presentation platform with an analytics subsystem that generates user profiles and interest clouds by analyzing user interaction data, identifying content features that meet interest thresholds and clustering media assets based on engagement patterns, using machine learning models to determine user and content clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems analyze user interaction data, then some insights are obtained, but the analysis efficiency and adequacy are insufficient
Solution Approach 1:
The patent segments user interaction data into multiple dimensions including content features, engagement metrics, and temporal patterns. The analytics subsystem divides the analysis into distinct processing stages: data collection, feature extraction, pattern recognition, and insight generation. This segmentation enables efficient processing while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as engagement thresholds, time windows, and weighting factors based on data characteristics and user behavior patterns. By changing parameters adaptively, the system optimizes both processing efficiency and measurement precision for different types of content and user interactions.
2Loss of information
If comprehensive user data is collected, then better user understanding is achieved, but system complexity increases
Solution Approach 1:
The analytics subsystem extracts only the most relevant features from comprehensive user interaction data, such as engagement duration, interaction frequency, and content category preferences. By taking out and focusing on key indicators rather than processing all raw data, the system maintains information completeness while reducing computational complexity.
Solution Approach 2:
The system performs preliminary data processing and feature extraction before main analysis, pre-organizing user interaction data into structured formats with identified patterns and relationships. This preliminary action reduces the complexity of subsequent analysis operations while preserving all essential user preference information.
3Productivity
If real-time analysis is implemented, then user engagement is improved, but processing load increases
Solution Approach 1:
The analytics subsystem implements periodic analysis at strategically determined intervals based on user activity patterns and content update frequencies. Rather than continuous processing, the system performs analysis periodically when meaningful changes occur, achieving real-time responsiveness while significantly reducing computational energy consumption.
Solution Approach 2:
The system applies partial analysis to high-value user interactions and full analysis only when necessary, using selective processing based on engagement thresholds and user importance. This approach provides real-time analysis capability for critical events while conserving computational resources during lower-priority periods.
Data Source
AI summary
Methods, systems, and apparatuses for user engagement analysis are described herein. An analytics subsystem may use a plurality of activity data to generate a plurality of user profiles, corresponding user interest clouds for each user device of a plurality of user devices, and a first interest cloud associated with a particular client identifier. The analytics subsystem may generate a second interest cloud associated based on a subset of a plurality of media assets associated with a threshold quantity of engagements. The analytics subsystem may determine a plurality of clusters of the plurality of media assets and may generate a content interest cloud for each of the plurality of clusters.