Central-Rank-Based Collection Selection in Uncooperative Distributed Information Retrieval

• Published in: Advances in Information Retrieval Vol. 4425; pp. 160 - 172
• Main Authors: Amati, Giambattista, Carpineto, Claudio, Romano, Giovanni
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2007; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Good Collection; Relevance Judgment; Relevant Document; Resource Selection; Retrieval Model
• ISBN: 9783540714941; 3540714944
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-71496-5_17

Collection selection is one of the key problems in distributed information retrieval. Due to resource constraints it is not usually feasible to search all collections in response to a query. Therefore, the central component (broker) selects a limited number of collections to be searched for the submitted queries. During the past decade, several collection selection algorithms have been introduced. However, their performance varies on different testbeds. We propose a new collection-selection method based on the ranking of downloaded sample documents. We test our method on six testbeds and show that our technique can significantly outperform other state-of-the-art algorithms in most cases. We also introduce a new testbed based on the trec gov2 documents.