From "Identical" to "Similar": Fusing Retrieved Lists Based on Inter-Document Similarities
Abstract
Methods for fusing document lists that were retrieved in response to a query often utilize the retrieval scores and/or ranks of documents in the lists. We present a novel fusion approach that is based on using, in addition, information induced from inter-document similarities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevance-status propagation between documents. The propagation is governed by inter-document-similarities and by retrieval scores of documents in the lists. Empirical evaluation demonstrates the effectiveness of our methods in fusing TREC runs. The performance of our most effective methods transcends that of effective fusion methods that utilize only retrieval scores or ranks.
Cite
Text
Kozorovitzky and Kurland. "From "Identical" to "Similar": Fusing Retrieved Lists Based on Inter-Document Similarities." Journal of Artificial Intelligence Research, 2011. doi:10.1613/JAIR.3214Markdown
[Kozorovitzky and Kurland. "From "Identical" to "Similar": Fusing Retrieved Lists Based on Inter-Document Similarities." Journal of Artificial Intelligence Research, 2011.](https://mlanthology.org/jair/2011/kozorovitzky2011jair-identical/) doi:10.1613/JAIR.3214BibTeX
@article{kozorovitzky2011jair-identical,
title = {{From "Identical" to "Similar": Fusing Retrieved Lists Based on Inter-Document Similarities}},
author = {Kozorovitzky, Anna Khudyak and Kurland, Oren},
journal = {Journal of Artificial Intelligence Research},
year = {2011},
pages = {267-296},
doi = {10.1613/JAIR.3214},
volume = {41},
url = {https://mlanthology.org/jair/2011/kozorovitzky2011jair-identical/}
}