Name
Abstract | ||
|---|---|---|
As humans, we love to rank things. Top ten lists exist for everything from movie stars to scary animals. Ambiguities (i.e., ties) naturally occur in the process of ranking when people feel they cannot distinguish two items. Human reported rankings derived from star ratings abound on recommendation websites such as Yelp and Netflix. However, those websites differ in star precision which points to the need for ranking systems that adapt to an individual user's preference sensitivity. In this work we propose an adaptive system that allows for ties when collecting ranking data. Using this system, we propose a framework for obtaining computer-generated rankings. We test our system and a computer-generated ranking method on the problem of evaluating human attractiveness. Extensive experimental evaluations and analysis demonstrate the effectiveness and efficiency of our work. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICME.2014.6890147 | ICME |
Keywords | DocType | ISSN |
ranking,Yelp Web site,face recognition,facial attractiveness,ranking data collection,adaptive ranking method,Netflix Web site,recommender systems,human attractiveness evaluation,computer-generated ranking method,Web sites,rating,recommendation system,adaptive methods | Conference | 1945-7871 |
Citations | PageRank | References |
12 | 0.62 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chong Cao | 1 | 14 | 1.69 |
| Iljung Sam Kwak | 2 | 12 | 0.62 |
| Serge J. Belongie | 3 | 12512 | 1010.13 |
| David Kriegman | 4 | 7693 | 451.96 |
| Haizhou Ai | 5 | 1742 | 116.51 |