Workshop in Biostatistics

DATE: October 20, 2016
TIME: 1:30 - 2:50 pm
LOCATION: Medical School Office Building, Rm x303
TITLE: Deep Learning for Large Scale Music Recommendation

Oriol Nieto
Data Scientist, Pandora


Music streaming services nowadays offer large catalogues that expose new challenges in terms of automatically recommending music to tens of millions of users. At Pandora, around 75 billion "thumbs" and manual analyses for over 1.5 million songs have been gathered over the years. In this workshop we will leverage some of these data by applying deep convolutional neural networks to both audio content and metadata with the goal of improving the listener experience. The basics of collaborative filtering [1] and deep learning for music recommendation [2] will be revised using Pandora data, along with multiple audio examples and open-ended discussions about how the "ideal" music recommendation system should behave.

Suggested readings:

[1]  Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. IEEE Computer Journal, 42(8), 42–49.

[2]  Oord, A. Van Den, Dieleman, S., & Schrauwen, B. (2013). Deep Content-based Music Recommendation. Advances in Neural Information Processing Systems, 2643–2651.