Evaluating popularity data for relevance ranking in library information systems

What is it about?

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.

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