Evaluating Score Normalization Methods in Data Fusion

Shengli Wu, Fabio Crestani, Yaxin Bi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Citations (Scopus)

Abstract

In data fusion, score normalization is a step to make scores, which are obtained from different component systems for all documents, comparable to each other. It is an indispensable step for effective data fusion algorithms such as CombSum and CombMNZ to combine them. In this paper, we evaluate four linear score normalization methods, namely the fitting method, Zero-one, Sum, and ZMUV, through extensive experiments. The experimental results show that the fitting method and Zero-one appear to be the two leading methods.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherSpringer
Number of pages6
Publication statusPublished - 2006
EventAIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology -
Duration: 1 Jan 2006 → …

Conference

ConferenceAIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Period1/01/06 → …

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