Messaging User Clustering via Activity Pattern Segmentation
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Solution Overview
Problem
Conventional approaches for determining user interests in content sharing platforms rely heavily on market research and demographic data, which are inadequate in capturing the unique and high-volume data available in messaging systems.
Innovation Solution
A quantitative data approach is employed to capture and analyze user activity patterns in messaging systems, clustering users based on consistent activity patterns across various time periods to characterize their behavioral personas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional market research and demographic data approaches are used to determine user interests, then the implementation is simple and cost-effective, but the data captured is inadequate and fails to reflect unique messaging system behavior
Solution Approach 1:
The patent segments user activity data into discrete temporal units (time bins) and activity types, creating a structured representation of user behavior that can be analyzed systematically. This segmentation transforms continuous messaging activity into quantizable units suitable for pattern recognition and clustering algorithms.
Solution Approach 2:
The patent introduces temporal dimensionality by analyzing user activity across multiple time periods and creating time-based clusters. This transforms static demographic data into dynamic behavioral patterns, adding the dimension of time to user characterization and enabling the detection of temporal activity patterns that conventional approaches miss.
2Loss of information
If messaging system activity data is analyzed to extract behavioral patterns, then user interest characterization improves significantly, but the computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining time bins and activity categories before analyzing user data. This preprocessing structure enables more efficient processing during the actual analysis phase, as the framework for pattern recognition is already established, reducing computational overhead during execution.
Solution Approach 2:
The patent creates simplified representations (copies) of user behavior through clustering algorithms that generate representative activity patterns. These cluster profiles serve as compressed versions of individual user behaviors, enabling efficient storage and comparison while retaining essential behavioral characteristics.
3Reliability
If clusters are generated based on consistent activity patterns across multiple time periods, then behavioral persona accuracy improves, but the computational complexity of pattern recognition increases
Solution Approach 1:
The patent changes parameters by establishing minimum thresholds for pattern consistency (requiring patterns to appear in a minimum number of time periods). This parameter adjustment filters out noise and偶然 behaviors, ensuring that only consistent, reliable patterns form the basis of user clusters, thereby improving reliability while maintaining manageable complexity.
Data Source
AI summary
A system analyzes user activity data generated by computing devices associated with a plurality of users in a messaging system to extract a random user from the plurality of users. Based on determining that user activity data associated with the random user comprises a consistent pattern, a cluster associated with the consistent pattern is generated and the random user is added to the cluster. Then user activity data for the other users in the plurality of users is analyzed to determine whether user activity data for each of the other users comprises a similar pattern as the generated cluster. Each user that is determined to be associated with user activity data comprising a similar pattern as the consistent pattern of the generated cluster is added to the generated cluster and user activity data associated with each user added to the generated cluster is removed from the user activity data.