Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/87276
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dc.contributor.authorPulis, Michael-
dc.contributor.authorBajada, Josef-
dc.date.accessioned2022-01-19T12:58:07Z-
dc.date.available2022-01-19T12:58:07Z-
dc.date.issued2021-
dc.identifier.citationPulis, M., & Bajada, J. (2021). Siamese neural networks for content-based cold-start music recommendation. RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam. 719-723.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/87276-
dc.description.abstractMusic 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. We propose the use of Siamese Neural Networks (SNNs) to determine the similarity between two audio clips. Each clip is first pre-processed into a Mel-Spectrogram, which is then used as input to an SNN consisting of two identical Convolutional Neural Networks (CNNs). The output of each CNN is then compared together to determine whether two songs are similar or not. These were trained using audio from the Free Music Archive, with the genre used as a heuristic to determine the similarity between song pairs. A query-by-multiple-example (QBME) music recommendation system was developed that makes use of the proposed content based similarity metric to find songs that match the user’s tastes. This was packaged inside an online blind-test survey, which first prompts participants to select a set of preferred songs, and then recommends a number of songs which the subject is expected to listen to and rate on a Likert scale. The recommendations from the proposed algorithm were stochastically interleaved with songs selected randomly from the preferred genres of the user, as a baseline for comparison. The participants were not aware that the recommendations came from two different algorithms. Our findings show that 60.7% of the 150 participants gave higher ratings to the recommendations made by the proposed SNN-based algorithm. Findings also show that 55% of the recommended songs had less than 1,500 listens, demonstrating that the proposed content based approach can provide a fairer exposure to all artists based on their music, independent of their fame and popularity.en_GB
dc.language.isoenen_GB
dc.publisherAssociation for Computing Machineryen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectMachine learningen_GB
dc.subjectDeep learning (Machine learning)en_GB
dc.subjectRecommender systems (Information filtering)en_GB
dc.subjectNeural networks (Computer science)en_GB
dc.subjectArtificial intelligenceen_GB
dc.titleSiamese neural networks for content-based cold-start music recommendationen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencenameRecSys '21: Fifteenth ACM Conference on Recommender Systemsen_GB
dc.bibliographicCitation.conferenceplaceAmsterdam, Netherlands, 27/09-01/10/2021en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1145/3460231.3478847-
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