This paper is concerned with the generalization ability of learning to rank algorithms for information retrieval (IR). We point out that the key for addressing the learning proble...
Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang...
We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online learning algorithms. The a...
Despite the widespread use of BM25, there have been few studies examining its effectiveness on a document description over single and multiple field combinations. We determine t...
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
In this poster,we incorporate user query history, as context information, to improve the retrieval performance in interactive retrieval. Experiments using the TREC data show that ...