From ranking algorithms to ‘ranking cultures’

Supervised ranking from semantics to algorithms

Ranking Algorithms by Performance

A common way of doing algorithm selection is to train a machine learning model and predict the best algorithm from a portfolio to solve a particular problem. While this method has been highly successful, choosing only a single algorithm has inherent limitations – if the choice was bad, no remedial action can be taken and parallelism cannot be exploited, to name but a few problems. In this paper...

A Survey of Ranking Algorithms

With the huge number of web pages that exist today, search engines assume an important role in the current Internet. But even if they allow to find relevant pages for any search topic, nowadays the number of results returned is often too big to be carefully explored. Moreover, the needs of the users vary, so that what may be interesting for one may be completely irrelevant for another. The role...

The Asymptotics of Ranking Algorithms

We consider the predictive problem of supervised ranking, where the task is to rank sets of candidate items returned in response to queries. Although there exist statistical procedures that come with guarantees of consistency in this setting, these procedures require that individuals provide a complete ranking of all items, which is rarely feasible in practice. Instead, individuals routinely pr...

Label Ranking Algorithms: A Survey

Label ranking is a complex prediction task where the goal is to map instances to a total order over a finite set of predefined labels. An interesting aspect of this problem is that it subsumes several supervised learning problems such as multiclass prediction, multilabel classification and hierarchical classification. Unsurpisingly, there exists a plethora of label ranking algorithms in the lit...