Collection selection and result merging are two major sub-problems in the field of distributed information retrieval (DIR). Computing cost, retrieval precision and retrieval recall are three main evaluation indexes in DIR. This paper develops a multi-variable quantitative partial differential equation (PDE) model, linking collection selection method and result merging method with cost, precision and recall indexes. The model is inspired by the Laplace equations, and can be applied more generally to interactions between these variables in order to uncover how the different methods of collection selection and result merging influence the three evaluation indexes of the DIR. Experiments are then conducted to determine the empirical and practical evaluate performance of the model. Experimental results on 50 topics of TREC indicate that the multi-variable PDE model of evaluation in DIR has a good performance and is a practical alternative.
Key words: Distributed information retrieval, collection selection, result merging, partial differential equation, PDE.
Copyright © 2022 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0