Deep Learning in Music Recommendation Systems
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TLDR
This review article explains particularities of the music domain in RS research and gives an overview of the state of the art that employs deep learning for music recommendation, in particular in the context of the current research on deep learning.Abstract:
Like in many other research areas, deep learning (DL) is increasingly adopted in music recommender systems (MRS). Deep neural networks are used in this area particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music items (tracks or artists) from music playlists or listening sessions. Latent item factors are commonly integrated into content-based filtering and hybrid MRS, whereas sequence models of music items are used for sequential music recommendation, e.g., automatic playlist continuation. This review article explains particularities of the music domain in RS research. It gives an overview of the state of the art that employs deep learning for music recommendation. The discussion is structured according to the dimensions of neural network type, input data, recommendation approach (content-based filtering, collaborative filtering, or both), and task (standard or sequential music recommendation). In addition, we discuss major challenges faced in MRS, in particular in the context of the current research on deep learning.read more
Citations
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Recommender Systems Leveraging Multimedia Content
TL;DR: A thorough review of the state-of-the-art of recommender systems that leverage multimedia content is presented, by classifying the reviewed papers with respect to their media type, the techniques employed to extract and represent their content features, and the recommendation algorithm.
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Investigating gender fairness of recommendation algorithms in the music domain
Alessandro B. Melchiorre,Navid Rekabsaz,Emilia Parada-Cabaleiro,Stefan Brandl,Oleg Lesota,Markus Schedl +5 more
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Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.
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An Interpretable Deep Learning Model for Automatic Sound Classification
TL;DR: This work proposes a novel interpretable deep learning model for automatic sound classification, which explains its predictions based on the similarity of the input to a set of learned prototypes in a latent space by designing a frequency-dependent similarity measure and by considering different time-frequency resolutions in the feature space.
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User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues
TL;DR: A novel approach to jointly model users by their musical preferences and cultural backgrounds and shows that incorporating both acoustic information of the tracks a user has listened to as well as the cultural background of users in the form of a music-cultural user model contributes to improved recommendation performance.
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