Learning a Gaussian Process Prior for Automatically Generating Music Playlists
نویسندگان
چکیده
This paper presents AutoDJ: a system for automatically generating music playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further introduces Kernel Meta-Training, which is a method of learning a Gaussian Process kernel from a distribution of functions that generates the learned function. For playlist generation, AutoDJ learns a kernel from a large set of albums. This learned kernel is shown to be more effective at predicting users’ playlists than a reasonable hand-designed kernel.
منابع مشابه
Towards Music Captioning: Generating Music Playlist Descriptions
Descriptions are often provided along with recommendations to help users’ discovery. Recommending automatically generated music playlists (e.g. personalised playlists) introduces the problem of generating descriptions. In this paper, we propose a method for generating music playlist descriptions, which is called as music captioning. In the proposed method, audio content analysis and natural lan...
متن کاملSteerable Playlist Generation by Learning Song Similarity from Radio Station Playlists
This paper presents an approach to generating steerable playlists. We first demonstrate a method for learning song transition probabilities from audio features extracted from songs played in professional radio station playlists. We then show that by using this learnt similarity function as a prior, we are able to generate steerable playlists by choosing the next song to play not simply based on...
متن کاملUnderstanding Music Playlists
As music streaming services dominate the music industry, the playlist is becoming an increasingly crucial element of music consumption. Consequently, the music recommendation problem is often casted as a playlist generation problem. Better understanding of the playlists is therefore necessary for developing better playlist generation algorithms. In this work, we analyse two playlist datasets to...
متن کاملLearning 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...
متن کاملAnalyzing the Characteristics of Shared Playlists for Music Recommendation
The automated generation of music playlists – as supported by modern music services like last.fm or Spotify – represents a special form of music recommendation. When designing a “playlisting” algorithm, the question arises which kind of quality criteria the generated playlists should fulfill and if there are certain characteristics like homogeneity, diversity or freshness that make the playlist...
متن کامل