Fiana Raiber

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Predicting <i>query performance</i>, that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. We present a novel approach to this task that is based on measuring the standard deviation of retrieval scores in the result list of the documents most highly ranked. We argue that for retrieval methods(More)
The query-performance prediction task is estimating the effectiveness of a search performed in response to a query when no relevance judgments are available. Although there exist many effective prediction methods, these differ substantially in their basic principles, and rely on diverse hypotheses about the characteristics of effective retrieval. We present(More)
How can a search engine with a relatively weak relevance ranking function compete with a search engine that has a much stronger ranking function? This dual challenge, which to the best of our knowledge has not been addressed in previous work, entails an interesting bi-modal utility function for the weak search engine. That is, the goal is to produce in(More)
Using relevance feedback can significantly improve the effectiveness of ad hoc (query-based) retrieval. However, retrieval performance can significantly vary with respect to the given set of relevant documents. Our goal is to establish a quantitative analysis of what makes a relevant document a good <i>representative</i> of the relevant-documents set(More)
In adversarial and noisy search settings as the Web, the document-query surface level similarity can be a highly misleading relevance signal. Thus, devising <i>content-based</i> relevance estimation (ranking) approaches becomes highly challenging. We address this challenge using two methods that utilize inter-document similarities in an initially retrieved(More)
We present a study of the correlation between the extent to which the cluster hypothesis holds, as measured by various tests, and the relative effectiveness of cluster-based retrieval with respect to document-based retrieval. We show that the correlation can be affected by several factors, such as the size of the result list of the most highly ranked(More)
We show that two tasks which were independently addressed in the information retrieval literature actually amount to the exact same task. The first is query performance prediction; i.e., estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments. The second task is cluster ranking, that is, ranking(More)