Embedding Songs and Tags for Playlist Prediction
نویسندگان
چکیده
Automatic playlist generation can be a useful tool to navigate the myriad choices available to users in music services today. Here, we present our recent work on explicitly modeling playlists without requiring external similarity measures. Our Logistic Markov Embedding is trained directly on historical playlist data and can unify songs and (when available) social tags in a Euclidean space. The resulting space can be used to generate playlists, perform tag-based retrieval tasks, or to visualize songs and tags.
منابع مشابه
Learning to Embed Songs and Tags for Playlist Prediction
Automatically generated playlists have become an important medium for accessing and exploring large collections of music. In this paper, we present a probabilistic model for generating coherent playlists by embedding songs and social tags in a unified metric space. We show how the embedding can be learned from example playlists, providing the metric space with a probabilistic meaning for song/s...
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