We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [19], we develop and imple...
This paper addresses the desktop search problem by considering various techniques for ranking results of a search query over the file system. First, basic ranking techniques, whic...
In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work ha...
Jingfang Xu, Chuanliang Chen, Gu Xu, Hang Li, Elbi...
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking st...
Fabio F. Faria, Adriano Veloso, Humberto Mossri de...