Manages comprehensive track history data collection, storage, and analysis
with a focus on track skipping behavior and listening patterns over time.
Features:
Detailed skip event recording with rich metadata
Chronological track history management
Time-based skip pattern analysis
Context-aware skip tracking (playlist, album, etc.)
Skip classification with confidence ratings
Multi-dimensional history data structures
Skip event aggregation and trend analysis
Sophisticated storage management for history data
Device and session tracking across listening periods
This module serves as the persistent storage layer for the playback
monitoring system, recording detailed information about skipped tracks
and listening patterns over time. It maintains rich contextual data
that enables sophisticated analytics and insights into user listening
behavior, focusing particularly on skip patterns and preferences.
The history system captures multiple dimensions of skipping behavior:
When tracks are skipped (time of day, session context)
How tracks are skipped (manual vs. automatic, progress percentage)
Where tracks are skipped (device, playlist, listening context)
Patterns in skipping (repeat skips, genre preferences, artists)
Description
Playback History Management Module
Manages comprehensive track history data collection, storage, and analysis with a focus on track skipping behavior and listening patterns over time.
Features:
This module serves as the persistent storage layer for the playback monitoring system, recording detailed information about skipped tracks and listening patterns over time. It maintains rich contextual data that enables sophisticated analytics and insights into user listening behavior, focusing particularly on skip patterns and preferences.
The history system captures multiple dimensions of skipping behavior: