Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals

@inproceedings{Kamehkhosh2016PersonalizedNM,
  title={Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals},
  author={Iman Kamehkhosh and Dietmar Jannach and Lukas Lerche},
  booktitle={UMAP},
  year={2016}
}
The automated generation of playlists given a user’s most recent listening history is a common feature of modern music streaming platforms. In the research literature, a number of algorithmic proposals for this “next-track recommendation” problem have been made in recent years. However, nearly all of them are based on the user’s most recent listening history, context, or location but do not consider the users’ long-term listening preferences or social network. In this work, we explore the value… CONTINUE READING

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References

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