A central problem in recommender systems and multi-criteria decision making is the task of aggregating multiple input rankings into a single output ranking. As a simple example, consider hotel booking websites such as booking.com. These websites offer various possible ways to order the different hotels, e.g., by price, by rating, or by a combination of these conditions. The latter is exactly the central problem of this project as the rankings arising from multiple criteria need to be combined into a single output ranking.
The aim of this project is to design and analyze methods for rank aggregation through the lens of social choice theory. Specifically, the goal is to find mechanisms for rank aggregation that can be shown to satisfy desirable properties, such as fairness, efficiency, or immunity to strategic misrepresentation of input rankings. There will also be the possibility to implement state of the art mechanisms for rank aggregation and experimentally analyze their performance. The student should preferably have taken courses in algorithm design/computational complexity or software engineering, and have an interest in game theory. An ideal but not necessary preparation for this project is the course "Knowledge representation and reasoning". Strong mathematical background with an interest in writing mathematical proofs is expected.
School
Computer Science and Engineering
Research Area
Algorithms | Algorithmic game theory
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
- The algorithmic decision theory (ADT) group has significant experience in computational social choice. The successful candidate will work closely with the supervisors.
The expected outcome of the project will be a technical report which surveys the algorithms considered during the project as well as an understanding of some of they key issues involved in the field of rank aggregation. Major outcomes will be theoretical results or implementation of some state-of-the-art rank aggregation algorithms and evaluation of their relative performance over various metrics.
