Module statistics/pattern-detector

Description

Skip Pattern Detection Service

This module implements advanced pattern recognition algorithms that analyze listening and skip behavior to identify meaningful insights about user preferences and habits. It uses statistical analysis and machine learning techniques to discover patterns that may not be immediately obvious from raw data.

Features:

• Multi-dimensional pattern detection across various metrics
• Confidence scoring for identified patterns
• Artist aversion/preference detection
• Temporal pattern recognition (time of day, day of week)
• Context-aware analysis (playlist vs. album behavior)
• Sequential behavior analysis (skip streaks, session patterns)
• Immediate vs. delayed skip classification
• Genre and mood preference identification
• Pattern persistence for trend analysis over time

The pattern detection engine uses configurable thresholds to identify statistically significant patterns while filtering out noise. Each pattern is assigned a confidence score based on:

1. Number of unique occurrences
2. Consistency of the observed behavior
3. Statistical deviation from baseline behavior
4. Recency and frequency of observations

Detected patterns are categorized by type and can be used to:

• Provide personalized insights to users
• Power recommendation systems
• Generate detailed analytics reports
• Identify changing preferences over time