Rank aggregation is the problem of generating an overall ranking from a set of individual votes which is as close as possible to the (unknown) correct ranking. The challenge is that votes are often both noisy and incomplete. Existing work focuses on the most likely ranking for a particular noise model. Instead, we focus on minimising the error, i.e., the expected distance between the aggregated ranking and the correct one. We show that this results in different rankings, and we show how to compute local improvements of rankings to reduce the error. Extensive experiments on both synthetic data based on Mallows' model and real data show that Copeland has a smaller error than the Kemeny rule, while the latter is the maximum likelihood estimator.
Original languageEnglish
Number of pages2
Publication statusPublished - 2016
EventAAMAS 2016 : 15th International Conference on Autonomous Agents and Multiagent Systems - Singapore, Singapore
Duration: 9 May 201613 May 2016
Conference number: 15


ConferenceAAMAS 2016
Abbreviated titleAAMAS
Internet address

    Research areas

  • Economic paradigms, Social Choice Theory

ID: 30557605