Automatic music recommendation has become an increasingly relevant problem in recent
years, since a lot of music is now sold and consumed digitally. Most recommender systems …

We introduce the Million Song Dataset Challenge: a large-scale, personalized music
recommendation challenge, where the goal is to predict the songs that a user will listen to …

How do we accurately browse a large set of images or efficiently annotate the images from
an image library? Image clustering methods are invaluable tools for applications such as …

Very few large-scale music research datasets are publicly available. There is an increasing
need for such datasets, because the shift from physical to digital distribution in the music …

Retrieval tasks typically require a ranking of items given a query. Collaborative filtering
tasks, on the other hand, learn to model user's preferences over items. In this paper we study …

Related US Application Data (60) Provisional application No. 61/956,354, filed on Jun. A
technique for determining annotation items associated with video information is described …

This work devises a factorization model called compact latent factor model, in which we
propose a compact representation to consider query, user and item in the model. The blend …

Most music streaming services rely on automatic recommendation algorithms to exploit their
large music catalogs. These algorithms aim at retrieving a ranked list of music tracks based …

We address the problem of disambiguating large scale catalogs through the definition of an
unknown artist clustering task. We explore the use of metric learning techniques to learn …

BACKGROUND A growing number of services provide large collections of music over the
Internet. As more and more artists create music, the sizes of these collections continue to …