We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
We convert cell-probe lower bounds for polynomial space into stronger lower bounds for near-linear space. Our technique applies to any lower bound proved through the richness meth...
Traditional information retrieval techniques based on keyword search help to identify a ranked set of relevant documents, which often contains many documents in the top ranks that...
We describe a framework for automatically selecting a summary set of photos from a large collection of geo-referenced photographs. Such large collections are inherently difficult ...
Alexander Jaffe, Mor Naaman, Tamir Tassa, Marc Dav...
In this paper, we address the problems of adaptive schema mappings between different peers in peer-to-peer network and searching for interesting data residing at different peers ba...