Classification-based resource selection

In some retrieval situations, a system must search across multiple collections. This task, referred to as federated search, occurs for example when searching a distributed index or aggregating content for web search. Resource selection refers to the subtask of deciding, given a query, which collections to search. Most existing resource selection methods rely on evidence found in collection content. We present an approach to resource selection that combines multiple sources of evidence to inform the selection decision. We derive evidence from three different sources: collection documents, the topic of the query, and query click-through data. We combine this evidence by treating resource selection as a multiclass machine learning problem. Although machine learned approaches often require large amounts of manually generated training data, we present a method for using automatically generated training data. We make use of and compare against prior resource selection work and evaluate acro...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Jaime Arguello, Jamie Callan, Fernando Diaz
• Keywords: CIKM 2009, Federated Search, Information Management, Resource Selection, Resource Selection Methods