Analysis of search stratagem utilisation

Abstract

In Interactive Information Retrieval, researchers consider the user behaviour towards systems and search tasks in order to adapt search results and to improve the search experience of users. Analysing the users' past interactions with the system is one typical approach. In this paper, we analyse the user behaviour in retrieval sessions towards Marcia Bates' search stratagems such as "Footnote Chasing", "Citation Searching", "Keyword Searching", "Author Searching" and "Journal Run" in a real-life academic search engine. In fact, search stratagems represent high-level search behaviour as the users go beyond simple execution of queries and investigate more of the system functionalities. We performed analyses of these five search stratagems using two datasets extracted from the social sciences search engine sowiport. A specific focus was the detection of the search phase and frequency of the usage of these stratagems. In addition, we explored the impact of these stratagems on the whole search process performance. We addressed mainly the usage patterns' observation of the stratagems, their impact on the conduct of retrieval sessions and explore whether they are used similarly in both datasets. From the observation and metrics proposed, we can conclude that the utilisation of search stratagems in real retrieval sessions leads to an improvement of the precision in terms of positive interactions. For both datasets (SUSS 14-15 and

Bibliometric-enhanced information retrieval: preface

“…It covers a variety of topics including: Authorship identification and writing style analysis (Rexha et al 2018); Locality sensitive hashing to measure the overlap bibliographic databases for paper indexing (Abdulhayoglu and Thijs 2018); Automatic discovery of cross-topic collaborations (Cagliero et al 2018); Identification of problems and their solutions in scientific articles (Heffernan and Teufel 2018) (Ma et al 2018); Detection of automatically generated sentences and texts within genuinely written papers (Tien and Labbé 2018); Citance-based retrieval and summarization using Information Retrieval and Machine Learning (Karimi et al 2018); Analysis of search stratagems for interactive information retrieval in bibliographic databases (Kacem and Mayr 2018). We hope the selection of papers in this special issue will be interesting and enjoyable for researchers coming from all relevant fields and provide a starting point for future explorations in the field.…”