Learning Collection FUsion Strategies for Information Retrieval

In this paper we describe an Information Retrieval problem called collection fusion. The collection fusion problem is to maximize the number of relevant natural language documents retrieved given: a natural language query, multiple collections of documents, and a fixed total number of documents to retrieve. We describe two algorithms that use past queries to learn collection fusion strategies. Tests of these algorithms on a corpus of 742,000 documents indicate that they can learn good fusion strategies. Moreover, the strategies learned by our methods are consistently superior to those learned by a standard learning algorithm.

• Type: Conference
• Year: 1995
• Where: ICML
• Authors: Geoffrey G. Towell, Ellen M. Voorhees, Narendra Kumar Gupta, Ben Johnson-Laird
• Keywords: Collection Fusion, Collection Fusion Problem, Fusion Strategies, ICML 1995, Machine Learning