Abstract
Playlists are a major way of interacting with music, as evidenced by the fact that streaming services currently host billions of playlists. In this content overload scenario, it is crucial to automatically characterise playlists, so that music can be effectively organised, accessed and retrieved. One way to characterise playlists is by their listening context. For example, one listening context is “workout”, which characterises playlists suited to be listened to by users while working out. Recent work attempts to predict the listening contexts of playlists, formulating the problem as multi-label classification. However, current classifiers for listening context prediction are limited in the input data modalities that they handle, and on how they leverage the inputs for classification. As a result, they achieve only modest performance. In this work, we propose to use knowledge graphs to handle multi-modal inputs, and to effectively leverage such inputs for classification. We formulate four novel classifiers which yield approximately 10% higher performance than the state-of-the-art. Our work is a step forward in predicting the listening contexts of playlists, which could power important real-world applications, such as context-aware music recommender systems and playlist retrieval systems.
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Notes
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Our formulation of MAP is different from others, which allow for multiple relevant items. In our case, there is only one relevant item: the correct listening context.
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Acknowledgements
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 12/RC/2289-P2 which is co-funded under the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. We are grateful to Elena Epure & Romain Hennequin from Deezer, for sharing the dataset we used in the experiments, and for assisting us in the process of replicating the baselines. We are also grateful to Jeong Choi from NAVER, for assisting us in the process of replicating the baselines.
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Gabbolini, G., Bridge, D. (2023). Predicting the Listening Contexts of Music Playlists Using Knowledge Graphs. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_21
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