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  1. Proceedings of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval (LSDS-IR '08)
  2. Integral based source selection for uncooperative distributed information retrieval environments
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Integral based source selection for uncooperative distributed information retrieval environments

Content Provider ACM Digital Library
Author Salampasis, Michail Satratzemi, Maria Paltoglou, Georgios
Abstract In this paper, a new source selection algorithm for uncooperative distributed information retrieval environments is presented. The algorithm functions by modeling each information source as an integral, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index and selects the collections that cover the largest area in the rank-relevance space. Based on the above novel metric, the algorithm explicitly focuses on addressing the two goals of source selection; high recall which is important for source recommendation applications and high precision aiming to produce a high precision final merged list. For the latter goal in particular, the new approach steps away from the usual practice of DIR systems of explicitly declaring the number of collections that must be queried and instead receives as input only the number of retrieved documents in the final merged list, dynamically calculating the number of collections that are selected and the number of documents requested from each. The algorithm is tested in a wide range of testbeds in both recall and precision oriented settings and its effectiveness is found to be equal or better than other state-of-the-art algorithms.
Starting Page 67
Ending Page 74
Page Count 8
File Format PDF
ISBN 9781605582542
DOI 10.1145/1458469.1458475
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2008-10-30
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Distributed information retrieval Federated search Source selection
Content Type Text
Resource Type Article
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