Taste over time: The temporal dynamics of user preferences

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Conference Proceeding

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We develop temporal embedding models for exploring how listening preferences of a population develop over time. In particular, we propose time-dynamic probabilistic embedding models that incorporate users and songs in a joint Eu-clidian space in which they gradually change position over time. Using large-scale Scrobbler data from Last.fm spanning a period of 8 years, our models generate trajectories of how user tastes changed over time, how artists developed, and how songs move in the embedded space. This ability to visualize and quantify listening preferences of a large population of people over a multi-year time period provides exciting opportunities for data-driven exploration of musicological trends and patterns.

Publication Name

Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013

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