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» Learning to rank for information retrieval
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SWAP
2008
15 years 7 months ago
Improving Retrieval Experience Exploiting Semantic Representation of Documents
The traditional strategy performed by Information Retrieval (IR) systems is ranked keyword search: for a given query, a list of documents, ordered by relevance, is returned. Releva...
Pierpaolo Basile, Annalina Caputo, Anna Lisa Genti...
SIGIR
1999
ACM
15 years 10 months ago
SCAN: Designing and Evaluating User Interfaces to Support Retrieval From Speech Archives
Previous examinations of search in textual archives have assumed that users first retrieve a ranked set of documents relevant to their query, and then visually scan through these ...
Steve Whittaker, Julia Hirschberg, John Choi, Dona...
WSDM
2010
ACM
1328views Data Mining» more  WSDM 2010»
16 years 3 months ago
TwitterRank: Finding Topic-sensitive Influential Twitterers
This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networkin...
Jianshu Weng, Ee-peng Lim, Jing Jiang, Qi He
WWW
2004
ACM
16 years 7 months ago
Ranking the web frontier
The celebrated PageRank algorithm has proved to be a very effective paradigm for ranking results of web search algorithms. In this paper we refine this basic paradigm to take into...
Nadav Eiron, Kevin S. McCurley, John A. Tomlin
JCDL
2005
ACM
100views Education» more  JCDL 2005»
15 years 12 months ago
Automatic extraction of titles from general documents using machine learning
In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of...
Yunhua Hu, Hang Li, Yunbo Cao, Dmitriy Meyerzon, Q...