Learning to Model Sequences:
Playlist Prediction for Local Music Discovery

NSF-Projects IIS-1217686 / IIS-1217485

2012-2016

Cornell University
Department of Computer Science

Ithaca College
Department of Computer Science

Project Goals

The ability to learn predictive models of sequences is a component in several application problems, ranging from language models for machine translation to the recommendation of playlists in online media systems. The goal of this project is to develop new machine learning algorithms that can learn sequence models for items that are difficult to describe by attributes. In particular, the project develops models that automatically embed items in a latent feature space based on training sequences, that can integrate partial and noisy side information, and that have the ability to model long-range dependencies. While the resulting models span a wide range of applications, the project focuses on the recommendation of music playlists as the main testbed. In particular, the project will develop and deploy an online music recommendation system for local music discovery.

People

Demos and Systems

Software and Data

Publications

[Schnabel/etal/15a] T. Schnabel, A. Swaminathan, T. Joachims, Unbiased Ranking Evaluation on a Budget, WWW Workshop on Offline and Online Evaluation of Web-based Services, 2015.
[PDF][BibTeX]
[Reddy/etal/15a] S. Reddy, I. Labutov, T. Joachims, Learning Representations of Student Knowledbe and Educational Content, ICML Workshop on Machine Learning for Education, 2015.
[PDF][BibTeX]
[Swaminathan/Joachims/15b] A. Swaminathan, T. Joachims, Counterfactual Risk Minimization: Learning from Logged Bandit Feedback, International Conference on Machine Learning (ICML), 2015.
[PDF][BibTeX]
[Sipos/etal/14a] R. Sipos, A. Ghosh, T. Joachims, Was This Review Helpful to You? It Depends! Context and Voting Patterns in Online Content, International World Wide Web Conference (WWW), 2014.
[PDF][BibTeX]
[Moore/etal/14a] J. L. Moore, T. Joachims, D. Turnbull, Taste Space Versus the World: an Embedding Analysis of Listening Habits and Geography, International Society for Music Information Retrieval (ISMIR) Conference, 2014.
[PDF][BibTeX] (Best Student Paper Award)
[Turnbull/etal/14a] D. Turnbull, J. Zupnick, K. Stensland, A. Horwitz, A. Wolf, A. Spirgel, S. Meyerhofer, T. Joachims, Using Personalized Radio to Enhance Local Music Discovery, Work in Progress Paper at ACM Conference on Human Factors in Computing Systems (CHI), 2014.
[PDF][BibTeX]
[Grant/etal/13a] M. Grant, A. Ekanayake, D. Turnbull, MeUse: Recommending Internet Radio Stations, International Conference of the International Society for Music Information Retrieval (ISMIR), 2013.
[PDF][BibTeX]
[Moore/etal/13a] J. Moore, S. Chen, T. Joachims, Douglas Turnbull, Taste over Time: the Temporal Dynamics of User Preferences, International Conference of the International Society for Music Information Retrieval (ISMIR), 2013.
[PDF][BibTeX]
[Chen/etal/13a] Shuo Chen, Jiexun Xu, T. Joachims, Multi-space Probabilistic Sequence Modeling, ACM Conference on Knowledge Discovery and Data Mining (KDD), 2013.
[PDF][BibTeX] [Software][Data]
[Chen/etal/12a] Shuo Chen, Joshua Moore, Douglas Turnbull, Thorsten Joachims, Playlist Prediction via Metric Embedding, ACM Conference on Knowledge Discovery and Data Mining (KDD), 2012.
[PDF]
 [BibTeX] [Software] [Data]
[Moore/etal/12b] J. Moore, Shuo Chen, T. Joachims, D. Turnbull, Learning to Embed Songs and Tags for Playlist Prediction, International Society for Music Information Retrieval Conference (ISMIR), 2012.
[PDF] [BibTeX] [Software] [Data]
[Moore/etal/12a] J. Moore, Shuo Chen, T. Joachims, D. Turnbull, Embedding Songs and Tags for Playlist Prediction, ICML Workshop on Machine Learning and Music, 2012.
[PDF]
 [BibTeX] [Software] [Data]

Related Courses

Related Presentations

Acknowledgement and Disclaimer

This material is based upon work supported by the National Science Foundation under Awards IIS-1217686 / IIS-1217485. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation (NSF).

Last change: 10/02/2015