Systems, methods and apparatus for generating music recommendations based on combining song and user influencers with channel rule characterizations

Abstract

Systems, methods and apparatus for generating music recommendations based on combining song and user influencers with channel rule characterizations are presented. Such systems and methods output a playlist, which may be delivered as an information stream of audio on a user or client device, such as a telephone or smartphone, tablet, computer or MP3 player, or any consumer device with audio play capabilities. The playlist may comprise various individual audio clips of one genre or type, such as songs, or of multiple types, such as music, talk, sports and comedy. The individual audio clips may be ordered by a sequencer, which, using large amounts of data, generates both (i) user independent and (i) user dependent influencer weightings for each clip, and then combines all of such influencer weightings into a combined play weighting W for a given audio clip, for a given user. Taking the various play weightings W(Ui, Sj), a set of rules may be applied to generate a set of candidates C(Ui, Sj, Tk) to play to User j in each of Time slots k through k+m. Real time playlists may then be generated from the m sets of candidates by application of a set of rules, which may be channel rules, for example. The data used to generate influencer weightings may include user-specific data including preferences and detailed listening history, audio clip specific data, and data gleaned from various Internet accessible sources, including social media. In some embodiments a feedback loop may be implemented to gauge the accuracy of the dynamically generated playlists and modify the influencer weightings in response.