We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these...
Christopher J. C. Burges, Tal Shaked, Erin Renshaw...
This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by...
Yu-Ting Liu, Tie-Yan Liu, Tao Qin, Zhiming Ma, Han...
Web spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the s...
Document ranking is well known to be a crucial process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. ...
Abstract. An open challenge in information distillation is the evaluation and optimization of the utility of ranked lists with respect to flexible user interactions over multiple ...