Modeling Musical Taste Evolution with Recurrent Neural Networks
This addresses the problem of predicting music preferences for listeners in streaming services, though it is incremental as it applies existing RNN methods to a new domain-specific dataset.
The paper tackled modeling musical taste evolution using a massive internet radio dataset and developed a recurrent neural network model for next station prediction, which outperformed baselines and excelled at long-tail personalization by learning long-term dependency structures.
Finding the music of the moment can often be a challenging problem, even for well-versed music listeners. Musical tastes are constantly in flux, and the problem of developing computational models for musical taste dynamics presents a rich and nebulous problem space. A variety of factors all play some role in determining preferences (e.g., popularity, musicological, social, geographical, generational), and these factors vary across different listeners and contexts. In this paper, we leverage a massive dataset on internet radio station creation from a large music streaming company in order to develop computational models of listener taste evolution. We delve deep into the complexities of this domain, identifying some of the unique challenges that it presents, and develop a model utilizing recurrent neural networks. We apply our model to the problem of next station prediction and show that it not only outperforms several baselines, but excels at long tail music personalization, particularly by learning the long-term dependency structure of listener music preference evolution.