Abstract
Nowadays, web search services are considered as a valuable tool for information exploration. However, due to the extremely rapid growth of the web, search engines cannot index the new information at the same time or with the same priority and thus their performance is hampered in some extend. Besides, web search services index the disseminated information with different algorithms, having as result different response time in updating their directories. In this paper, we propose a meta-search algorithm, which is capable of self-adapting over the continuous changes that occur on the indexed web, using a web evolution adaptation mechanism. This mechanism is put in the context of real capture–recapture experiments conducted in wildlife biological studies. The meta-search algorithm also supports a monitor mechanism, which records the user's browsing behavior during his search sessions. The paper provides the implementation details of the proposed meta-search ranking algorithm along with its initial assessment over health-related information using three medical web search services. We prove in our approach, that when user's browsing behavior is jointly used with a dynamic survey mechanism, which scores the ability of each search service to adapt in the incessant evolution of the web, a more effective meta-search is provided. Experimental results showed that the precision of the third-party results, were increased in several recall levels, for a tested period between September of 2006 and April of 2007.