Bibliometric-enhanced Information Retrieval

TL;DR

This paper discusses how bibliometric techniques like Bradfordizing and coauthorship network analysis can enhance information retrieval in digital libraries, aiming to bridge the gap between IR and bibliometrics for improved user services.

Contribution

It highlights the potential of integrating bibliometric methods into IR systems to improve retrieval accuracy and user experience in digital libraries.

Findings

Bibliometric techniques can enhance retrieval effectiveness.

Network analysis improves community-specific search results.

Statistical modeling offers value-added services for digital library users.

Abstract

Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of coauthorship network, can improve retrieval services for specific communities, as well as for large, cross-domain collections. This workshop aims to raise awareness of the missing link between information retrieval (IR) and bibliometrics/scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the digital library interface.