Siamese Neural Networks for Content-based Cold-Start Music Recommendation.
Michael Pulis, Josef Bajada
Abstract
Music recommendation systems typically use collaborative filtering to determine which songs to recommend to their users. This mechanism matches a user with listeners that have similar tastes, and uses their listening history to find songs that the user will probably like. The fundamental issue with this approach is that artists already need to have a significant user following to get a fair chance of being recommended. This is known as the music cold-start problem. In this work, we investigate the possibility of making music recommendations based on audio content so that new artists still get a good chance of being recommended, even if they do not have a sufficient number of listeners yet.
Topics & Concepts
Active listeningCollaborative filteringComputer scienceRecommender systemMultimediaContent (measure theory)Artificial neural networkSpeech recognitionInformation retrievalArtificial intelligencePsychologyCommunicationMathematicsMathematical analysisMusic and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing