Yahoo! music recommendations
- 23 October 2011
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 165-172
- https://doi.org/10.1145/2043932.2043964
Abstract
In the past decade large scale recommendation datasets were published and extensively studied. In this work we describe a detailed analysis of a sparse, large scale dataset, specifically designed to push the envelope of recommender system models. The Yahoo! Music dataset consists of more than a million users, 600 thousand musical items and more than 250 million ratings, collected over a decade. It is characterized by three unique features: First, rated items are multi-typed, including tracks, albums, artists and genres; Second, items are arranged within a four level taxonomy, proving itself effective in coping with a severe sparsity problem that originates from the unusually large number of items (compared to, e.g., movie ratings datasets). Finally, fine resolution timestamps associated with the ratings enable a comprehensive temporal and session analysis. We further present a matrix factorization model exploiting the special characteristics of this dataset. In particular, the model incorporates a rich bias model with terms that capture information from the taxonomy of items and different temporal dynamics of music ratings. To gain additional insights of its properties, we organized the KddCup-2011 competition about this dataset. As the competition drew thousands of participants, we expect the dataset to attract considerable research activity in the future.Keywords
This publication has 16 references indexed in Scilit:
- Matrix Factorization Techniques for Recommender SystemsComputer, 2009
- Regression-based latent factor modelsPublished by Association for Computing Machinery (ACM) ,2009
- Factorization meets the neighborhoodPublished by Association for Computing Machinery (ACM) ,2008
- Social Tagging and Music Information RetrievalJournal of New Music Research, 2008
- Lessons from the Netflix prize challengeACM SIGKDD Explorations Newsletter, 2007
- A Parallel Implementation of the Simplex Function Minimization RoutineComputational Economics, 2007
- Content-based TransformationsJournal of New Music Research, 2003
- Methods and metrics for cold-start recommendationsPublished by Association for Computing Machinery (ACM) ,2002
- Convergence Properties of the Nelder--Mead Simplex Method in Low DimensionsSIAM Journal on Optimization, 1998
- A Simplex Method for Function MinimizationThe Computer Journal, 1965