Extending Term Suggestion with Author Names
This work addresses a specific issue for users of digital libraries, particularly in social sciences, by improving query formulation through integrated author name and term suggestions, but it is incremental as it builds on existing polyrepresentation ideas.
The study tackled the problem of term suggestion modules neglecting author name queries in digital libraries, showing that adding topical-related author names to queries significantly increased retrieval performance when combined with thesaurus term expansion.
Term suggestion or recommendation modules can help users to formulate their queries by mapping their personal vocabularies onto the specialized vocabulary of a digital library. While we examined actual user queries of the social sciences digital library Sowiport we could see that nearly one third of the users were explicitly looking for author names rather than terms. Common term recommenders neglect this fact. By picking up the idea of polyrepresentation we could show that in a standardized IR evaluation setting we can significantly increase the retrieval performances by adding topical-related author names to the query. This positive effect only appears when the query is additionally expanded with thesaurus terms. By just adding the author names to a query we often observe a query drift which results in worse results.