Provides advanced visualization and analysis of algorithmically detected
patterns in user's track skipping behavior. This component helps users
understand their unconscious listening preferences and behaviors.
Features:
Pattern type distribution with multiple visualization options
Confidence level analysis for detected patterns
Detailed pattern descriptions with evidence and examples
Filtering capabilities by pattern type
Interactive expanding/collapsing of pattern details
Manual refresh for updated pattern detection
Multiple chart types (bar charts, pie charts, radial charts)
Loading skeleton state during data retrieval
Empty and error state handling
This component leverages machine learning algorithms to identify meaningful
patterns in skipping behavior, helping users gain deeper insights into their
listening habits that may not be immediately obvious through standard metrics.
Description
Skip Pattern Detection and Analysis Component
Provides advanced visualization and analysis of algorithmically detected patterns in user's track skipping behavior. This component helps users understand their unconscious listening preferences and behaviors.
Features:
This component leverages machine learning algorithms to identify meaningful patterns in skipping behavior, helping users gain deeper insights into their listening habits that may not be immediately obvious through standard metrics.