Large-scale analysis of query logs to profile users for dataset search

With an explosion of datasets available on the Web, dataset search has gained attention as an emerging research domain. Understanding users' dataset behaviour is imperative for providing effective data discovery services. In this paper, the authors present a study on users' dataset search behaviour through the analysis of search logs from a research data discovery portal.

Using query and session based features, the authors apply cluster analysis to discover distinct user profiles with different search behaviours. One particular behavioural construct of our interest is users' expertise that the authors generate via computing semantic similarity between users' search queries and the title of metadata records in the displayed search results.

The findings revealed that there are six distinct classes of user behaviours for dataset search, namely; Expert Research, Expert Search, Expert Explore, Novice Research, Novice Search and Novice Explore.

The user profiles are derived based on analysis of the search log of the research data catalogue in this study. Further research is needed to generalise the user profiles to other dataset search settings. Future research can take on a confirmatory approach to verify these user groups and establish a deeper understanding of their information needs.

The findings in this paper have implications for designing search systems that tailor search results matching the diverse information needs of different user groups.

We propose for the first time a taxonomy of users for dataset search based on their domain expertise and search behaviour.