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A document retrieval system should rank documents in order of their usefulness or satisfaction to the users. This principle was first explicated in the classic paper by Maron and Kuhns (1). Additional considerations concerning document ranking have been suggested by other researchers (2,3). Particular attention will be given here to the ranking algorithm appropriate for those presenting the same request, but having different information needs. The research on which this report is based identifies limitations associated with sequencing rules that use a probability ranking technique (4). Three basic and somewhat interdependent limitations will be discussed.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |