This collaborative research project involves faculty and students from Cornell University (IIS-1217686) and Ithaca College (IIS-1217485) in an interdisciplinary project. 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 material to read in order to master a subject. 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.
The resulting predictive models have a potential to be employed in science and education and can support the economic shift towards online business applications. The project focuses on the recommendation of music playlists as the main testbed. A deployed online music recommendation system not only provides the framework for testing and evaluation, this application domain helps to attract a broad spectrum of students from collaborating institutions, Cornell University and Ithaca College (an undergraduate institution), enabling the integration of undergraduates in the research. Project results, including open source software and annotated data set, are disseminated via the project Web site (www.cs.cornell.edu/People/tj/playlists/).
