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A theoretic framework for multimedia information retrieval is introduced which guarantees optimal retrieval effectiveness. In particular, a Ranking Principle for Distributed Multimedia-Documents (RPDM) is described together with an algorithm that satisfies this principle. Finally, the RPDM is shown to be a generalization of the Probability Ranking principle (PRP) which guarantees optimal retrieval effectiveness in the case of text document retrieval. The PRP justifies theoretically the relevance ranking adopted by modern search engines. In contrast to the classical PRP, the new RPDM takes into account transmission and inspection time, and most importantly, aspectual recall rather than simple recall.
submission for DL'99. conference compliant format (two-column, etc.) will be produced later
H.3.4, FOS: Computer and information sciences, H.3.3, H.2.4, H.3.3;H.3.4;H.3.7;H.2.4;C.2.4, H.3.7, C.2.4, Computer Science - Digital Libraries, Digital Libraries (cs.DL)
H.3.4, FOS: Computer and information sciences, H.3.3, H.2.4, H.3.3;H.3.4;H.3.7;H.2.4;C.2.4, H.3.7, C.2.4, Computer Science - Digital Libraries, Digital Libraries (cs.DL)
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 |