Playlist Classifier for Abusive User Account Detection

The patent replaces manual user reporting (mechanical human operation) with an automated machine learning system that uses trained classifiers to automatically detect and score spam playlists. The system processes thousands of playlists daily by calculating feature scores and channel scores through automated computational methods, eliminating the bottleneck of manual review while maintaining high identification accuracy.

The system enables self-service by allowing the platform to automatically identify and demote spam content without requiring user reports. The trained classifiers autonomously analyze playlist features, calculate scores, and determine which content to demote, making the spam detection process self-sufficient and scalable to handle thousands of daily spam playlists.

The patent applies preliminary anti-action by pre-training classifiers on spam playlist features before deployment. The system proactively identifies spam patterns and establishes scoring thresholds in advance, so when spam playlists are created, they are automatically detected and demoted before they can flood search results. This preventive approach counteracts the harmful effect of spam optimization while preserving legitimate content visibility.

The patent introduces an intermediary scoring system between content creation and search result display. The trained classifiers act as intermediaries that evaluate playlist quality through feature scoring and channel scoring, determining whether content should be promoted or demoted. This intermediary layer filters out spam content while allowing legitimate optimized content to maintain visibility, resolving the conflict between SEO benefits and spam prevention.

The patent segments the spam detection system into distinct modular components: feature extraction modules that identify playlist characteristics, trained classifier modules that score features, aggregation modules that calculate channel scores from multiple playlist scores, and demotion modules that apply penalties to search results. This segmentation allows each component to be independently trained, optimized, and maintained, reducing overall system complexity while achieving effective spam reduction.

The patent utilizes parameter changes by training classifiers on varying feature sets and adjusting scoring thresholds based on performance metrics. The system dynamically modifies parameters such as feature weights, score thresholds, and demotion penalties to optimize spam detection while controlling false positives. This parameter-based approach allows flexible adjustment of system complexity versus effectiveness without requiring complete system redesign.