A threshold rule applied to the retrieval decision model

The retrieval decision problem is considered from the viewpoint of a decision theory approach. A threshold rule based on a suggested rule for indexing is considered and analyzed for retrieval decisions. The threshold rule is seen as a good descriptive measure of what a reasonable retrieval system should be able to accomplish. A retrieval mechanism of randomly drawing documents is analyzed to determine the relative strength of the threshold rule. The Neyman‐Pearson lemma is shown to yield a better a priori decision rule for retrieval; maximize precision subject to a fixed level of recall, instead of setting a lower limit upon precision, as does the threshold rule. The threshold rule is seen as a necessary, but not sufficient, condition for effective retrieval. A sufficient condition for the threshold rule illustrates the relationship between it and the rule derived from the Neyman‐Pearson lemma. Finally, a new measure of information retrieval system performance is presented, based on the threshold rule.