entertainment experience MSc in Software Engineering industrial experience currently in academia PhD in Information Retrieval

man word processing, etc. However, even well-formed queries hit exists in a file fragment in unallocated space for which the file signature is overwritten. Search hit ranking algorithms are prevalent in tradi-tional information retrieval (IR) contexts, such as web browsing and digital library

In this paper we introduce a novel approach for query performance prediction based on ranking list scores dispersion. Starting from the premise that different score distributions appear for good and poor performance queries, we introduce a set of measures that capture these differences between both

We study the problem of organizing the documents returned by an information retrieval system in response to a natural language query. We consider two well-known approaches -- the ranked list and clustering of the results -- and we show how they can be integrated. This new procedure is designed

Search Engine Results (SER) ranked lists show only a limited view of the information space, do not show how similar the retrieved documents are and/or how the retrieved documents relate to each other [1,2]. 2. Objective SER graphs could present at a glance an overview of any clusters or isolated

in ranked lists that resolves these four issues. We demonstrate the implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences. We applied DRIM to ChIP–chip and CpG methylation data

for information systems and that the publication outlet is a more suitable indicator of quality. We present a ranking list of journals for the information systems discipline, and discuss the approach we have taken in developing the list. We discuss how the ranking list may be used in defining and measuring

In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search

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 ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data

We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations