Predicting the Listening Contexts of Music Playlists Using Knowledge Graphs

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.