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» Learning to rank for information retrieval
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VLDB
2004
ACM
129views Database» more  VLDB 2004»
15 years 3 months ago
Probabilistic Ranking of Database Query Results
We investigate the problem of ranking answers to a database query when many tuples are returned. We adapt and apply principles of probabilistic models from Information Retrieval f...
Surajit Chaudhuri, Gautam Das, Vagelis Hristidis, ...
ECIR
2008
Springer
14 years 11 months ago
An Evaluation Measure for Distributed Information Retrieval Systems
This paper is concerned with the evaluation of distributed and peer-to-peer information retrieval systems. A new measure is introduced that compares results of a distributed retrie...
Hans Friedrich Witschel, Florian Holz, Gregor Hein...
ECAI
2010
Springer
14 years 11 months ago
Learning Aggregation Functions for Expert Search
Abstract. Machine learning techniques are increasingly being applied to problems in the domain of information retrieval and text mining. In this paper we present an application of ...
Ronan Cummins, Mounia Lalmas, Colm O'Riordan
ICMLA
2009
14 years 7 months ago
Discovering Characterization Rules from Rankings
For many ranking applications we would like to understand not only which items are top-ranked, but also why they are top-ranked. However, many of the best ranking algorithms (e.g....
Ansaf Salleb-Aouissi, Bert C. Huang, David L. Walt...
CORR
2010
Springer
141views Education» more  CORR 2010»
14 years 10 months ago
A database approach to information retrieval: The remarkable relationship between language models and region models
In this report, we unify two quite distinct approaches to information retrieval: region models and language models. Region models were developed for structured document retrieval....
Djoerd Hiemstra, Vojkan Mihajlovic