PrefRank
Anirban Basu, Juan Camilo Corena, Shinsaku Kiyomoto, Jaideep S. Vaidya, Stephen Marsh, Yutaka Miyake · 2014
Ranking vast amounts of user-contributed content, such as digital photographs, is handled well through user-driven ranking, but user-driven ranking is often subjective and difficult to compare. The analytic hierarchy process helps making sense of subjective opinion, whereas finding a global ranking is a problem of rank aggregation of partially ranked lists. In this position paper, we propose a solution -- PrefRank -- based on eigenvector centrality that helps aggregating partially ranked lists. Our proposed approach can be used in other application scenarios involving qualitative judgement and ranking, such as reviewing academic papers for a conference.