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Evaluating popularity data for relevance ranking in library information systems
Author(s) -
Plassmeier Kim,
Borst Timo,
Behnert Christiane,
Lewandowski Dirk
Publication year - 2015
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2015.1450520100125
Subject(s) - popularity , relevance (law) , computer science , ranking (information retrieval) , raw data , data science , information retrieval , focus (optics) , citation , information system , scale (ratio) , data mining , world wide web , psychology , engineering , geography , social psychology , physics , electrical engineering , cartography , optics , political science , law , programming language
In this poster, we present our work in progress to develop a relevance model for library information systems, which takes non‐textual factors into account. Here we focus on popularity data like citation or usage data. These data contain various biases that need to be corrected so as not to degrade the performance of the relevance model. Further, the different data might be to some extent incommensurable. We make use of the Characteristic Scores and Scales method to achieve two goals: first, remove biases from the raw data, and second, establish a common scale for the different data to support weighing the data against each other.