US20240346072
2024-10-17
Physics
G06F16/638
A system has been developed to generate personalized music recommendations by combining song and user influencers with channel rule characterizations. The output is a playlist delivered as an audio stream to various devices, including smartphones, tablets, and computers. These playlists can consist of different types of audio clips, ranging from music to talk shows, sports, and comedy.
The playlist generation process utilizes a sequencer that analyzes extensive data to produce influencer weightings for each audio clip. These weightings are categorized into user-independent and user-dependent types. By applying a set of rules, the system generates a list of potential audio clips tailored to individual users for specific time slots, creating real-time playlists based on user preferences and listening history.
To create accurate influencer weightings, the system collects various data types, including user-specific preferences, detailed listening histories, and information from social media platforms. This comprehensive data collection enables the system to adapt playlists dynamically, enhancing user experience and engagement with the music service.
A feedback loop may be integrated into the system to assess the effectiveness of the generated playlists. By analyzing user interactions and satisfaction levels, the influencer weightings can be modified accordingly to improve future recommendations. This continuous improvement process aims to increase user loyalty to the music service.
The shift from traditional broadcast media to personalized streaming services presents challenges in managing large datasets for individual users. Effective methods are required for collecting, storing, processing, and analyzing this data to create optimal playlists that enhance user experience. The need for sophisticated algorithms to measure the performance of recommendation engines is also crucial for maintaining user engagement.