This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a ...
One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures used in i...
This paper investigates the agreement of relevance assessments between official TREC judgments and those generated from an interactive IR experiment. Results show that 63% of docu...
There has been recent interest in collecting user or assessor preferences, rather than absolute judgments of relevance, for the evaluation or learning of ranking algorithms. Since...
We investigate a representative case of sudden information need change of Web users. By analyzing search engine query logs, we show that the majority of queries submitted by users...
We introduce a novel approach to expert finding based on multi-step relevance propagation from documents to related candidates. Relevance propagation is modeled with an absorbing ...
We describe a method for applying parsimonious language models to re-estimate the term probabilities assigned by relevance models. We apply our method to six topic sets from test ...
Edgar Meij, Wouter Weerkamp, Krisztian Balog, Maar...
In this paper, we show how a user profile can be enhanced when a more detailed description of the products is included. Two main assumptions have been considered: the first implie...
Juan F. Huete, Luis M. de Campos, Juan M. Fern&aac...
Motivated by our work with political scientists who need to manually analyze large Web archives of news sites, we present SpotSigs, a new algorithm for extracting and matching sig...
Martin Theobald, Jonathan Siddharth, Andreas Paepc...
We demonstrate that regularization can improve feedback in a language modeling framework. Categories and Subject Descriptors: H.3.3 Information Search and Retrieval: Relevance Fee...