BSF: 2014391: Aggregation Methods for Partial Preferences Overview.

Drexel University, Philadelphia PA

Investigators

Abstract

Preferences are orders among a collection of items attributed to a population of judges. Preference data comes in a variety of forms, such as ranked lists and pairwise comparisons, and is ubiquitous in a plethora of applications across different domains. Useful and effective analysis of preference data typically involves various forms of aggregation among many judges. This work focuses on the analysis of incomplete preferences, including mining, clustering and aggregation. Models, algorithms, data and software products developed as part of this project will be made publicly available. The work will have an impact on the scientific community, in particular on the analysis of functional genomics data, which is central to many areas of bioinformatics, and on social applications, where it will enable efficient and effective analysis of user preferences. This project develops data analysis methodologies that are geared towards incomplete preferences. This work will establish principles, paradigms, and computing machinery for effective analysis of large datasets of incomplete preferences. To that aim, this project develops (1) novel approaches for mining frequent, or otherwise interesting, patterns in preference data; (2) novel clustering and preference aggregation methods; and (3) an extensive data acquisition and experimental evaluation to enhance the research and development of analysis methodologies. The PI will involve graduate and undergraduate students in her research, and will continue to work with women and under-represented minorities. Both the process and the outcome of this research will be integrated into data management and data science courses taught by the PI.