Probabilistic top-k ranking queries have been extensively studied due to the fact that data obtained can be uncertain in many real applications. A probabilistic top-k ranking quer...
This paper aims to tackle the very interesting and important problem of user personalized ranking of search results. The focus is on news retrieval and the data from which the ran...
RankBoost is a recently proposed algorithm for learning ranking functions. It is simple to implement and has strong justifications from computational learning theory. We describe...
Raj D. Iyer, David D. Lewis, Robert E. Schapire, Y...
In this paper we propose a method for predicting the ranking position of a Web page. Assuming a set of successive past top-k rankings, we study the evolution of Web pages in terms...
Michalis Vazirgiannis, Dimitris Drosos, Pierre Sen...
- The aim of this paper is to propose a filter, based on a multi-objective evolutionary algorithm, for attributes’ ranking in the context of a data mining task. The behavior of t...
Daniela Zaharie, Stefan Holban, Diana Lungeanu, Da...