The top-ranked documents from various information sources that are merged together into a unified ranked list may cover the same piece of relevant information, and cannot satisfy different user needs. Result diversification(RD) solves this problem by diversifying results to cover more information needs. In recent times, RD has attracted much attention as a means of increasing user satisfaction in general purpose search engines. A myriad of approaches have been proposed in the related works for the diversification problem. However, no concrete study of search result diversification has been done in a Distributed Information Retrieval(DIR) setting. In this paper, we survey, classify and propose a theoretical framework that aims at improving diversification at the result merging phase of a DIR environment.
Autonomic Supervision of Stigmergic Self-Organisation for Distributed Information Retrieval
