Modelling rank-probability of relevance relationship in resultant document list for data fusion
Shengli Wu, Yaxin Bi, Xiaoqin Zeng · 2010
In this paper we present a new data fusion method in information retrieval, which uses ranking information of resultant documents. Our method is based on the modelling of rank-probability of relevance of documents in resultant document list using logarithmic models. The proposed method is more effective than other data fusion methods which also use ranking information, and is as effective as some data fusion methods which rely on reliable scoring information.