Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item taxonomy

Gideon Dror, Noam Koenigstein, Yehuda Koren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationRecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems
Pages165-172
Number of pages8
DOIs
StatePublished - 2011
Event5th ACM Conference on Recommender Systems, RecSys 2011 - Chicago, IL, United States
Duration: 23 Oct 201127 Oct 2011

Publication series

NameRecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

Conference

Conference5th ACM Conference on Recommender Systems, RecSys 2011
Country/TerritoryUnited States
CityChicago, IL
Period23/10/1127/10/11

Keywords

  • Yahoo! music
  • collaborative filtering
  • matrix factorization
  • recommender systems

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