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Adaptive data fusion methods in information retrieval
Author(s) -
Wu Shengli,
Li Jieyu,
Zeng Xiaoqin,
Bi Yaxin
Publication year - 2014
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23140
Subject(s) - computer science , benchmark (surveying) , set (abstract data type) , sensor fusion , data set , data mining , fusion , information retrieval , data retrieval , artificial intelligence , linguistics , philosophy , geodesy , programming language , geography
Data fusion is currently used extensively in information retrieval for various tasks. It has proved to be a useful technology because it is able to improve retrieval performance frequently. However, in almost all prior research in data fusion, static search environments have been used, and dynamic search environments have generally not been considered. In this article, we investigate adaptive data fusion methods that can change their behavior when the search environment changes. Three adaptive data fusion methods are proposed and investigated. To test these proposed methods properly, we generate a benchmark from a historic T ext RE trieval Conference data set. Experiments with the benchmark show that 2 of the proposed methods are good and may potentially be used in practice.