Cumulated Gain-based Evaluation of IR Techniques-英文文献.pdf

Cumulated Gain-based Evaluation of IR Techniques Kalervo Järvelin Jaana Kekäläinen University of Tampere Department of Information Studies FIN-33014 University of Tampere FINLAND Email: {kalervo.jarvelin, jaana.kekalainen}@uta.fi Submitted to ACM Transactions on Information Systems 25.1.2002, revised 9.7.2002. Abstract Modern large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation to the users. In order to develop IR tech- niques to this direction, it is necessary to develop evaluation approaches and methods that credit IR methods for their ability to retrieve highly relevant documents. This can be done by extending traditional evaluation methods, i.e., recall and precision based on binary relevance assessments, to graded relevance assessments. Alternatively, novel measures based on graded relevance assessments may be developed. This paper proposes three novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. The first 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, based on the cumulative gain they are able to yield. The novel measures are defined and discussed and then their use is demonstrated in a case study using