Domenico Dato

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Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web collections where it involves effectiveness requirements and efficiency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of efficient and effective Learning to Rank(More)
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very effective for scoring query results returned by large-scale Web search engines. Unfortunately, the computational cost of scoring thousands of candidate documents by traversing large ensembles of trees is high. Thus, several works have investigated solutions(More)
Phishing is a serious threat to global security and economy. Previously we have developed a phishing filtering system based on automatic classification. We perform statistical filtering of emails, where a classifier is trained on characteristic features of existing emails and subsequently is able to identify new phishing emails with different contents. In(More)
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