Cumulated Gain-Based Evaluation of IR Techniques
In this paper, the author presented us something about the measurements of the novel, which compute the cumulative gain the user obtains and examine the retrieval result up to a given ranked position.
And large amount of the output has been confused by the users for a long time when using the IR system.
There are three new evaluation measures. The firs one accumulates the relevance scores of retrieved documents along the ranked result list. The second one is similar but applies a discount factor on the relevance scores in order to devaluate late-retrieved documents. The third one computes the relative-to-the-ideal performance of IR techniques. An essential feature of the proposed measures is the weighting of documents at different levels of relevance.
For example. the discounted cumulated gain:A discounting function is needed that progressively reduces the document score as its rank increases but not too steeply to allow for user persistence in examining further documents. A simple way of discounting with this requirement is to divide the document score by the log of its rank.
Relevance Judgments:Possible differences between IR techniques in retrieving highly relevant documents might be evened up by their possible indifference in retrieving marginal documents. The net differences might seem practically marginal and statistically insignificant.
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