Data fusion is currently used extensively in information retrievalfor 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 paper, we investigate adaptive data fusion methods that can changetheir behavior when the search environment changes.Three adaptive data fusion methods are proposed and investigated.In order to test these proposed methods properly, we generate a benchmark from ahistorical TREC data set. Experiments with the benchmark show that two of theproposed methods are good and may potentially be used in practice.
|Journal||Journal of the Association for Information Science and Technology|
|Publication status||Published - 1 Oct 2014|