Searching and stopping : an analysis of stopping rules and strategies

Maxwell, David and Azzopardi, Leif and Järvelin, Kalervo and Keskustalo, Heikki; (2015) Searching and stopping : an analysis of stopping rules and strategies. In: CIKM '15 : Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, New York, NY., pp. 313-322. ISBN 9781450337946 (https://doi.org/10.1145/2806416.2806476)

[thumbnail of Maxwell-etal-CIKM-2015-Searching-and-stopping-an-analysis-of-stopping-rules-and-strategies]
Preview
 Text. Filename: Maxwell_etal_CIKM_2015_Searching_and_stopping_an_analysis_of_stopping_rules_and_strategies.pdf
Accepted Author Manuscript
Download (743kB)| Preview

Abstract

Searching naturally involves stopping points, both at a query level (how far down the ranked list should I go?) and at a session level (how many queries should I issue?). Understanding when searchers stop has been of much interest to the community because it is fundamental to how we evaluate search behaviour and performance. Research has shown that searchers find it difficult to formalise stopping criteria, and typically resort to their intuition of what is "good enough". While various heuristics and stopping criteria have been proposed, little work has investigated how well they perform, and whether searchers actually conform to any of these rules. In this paper, we undertake the first large scale study of stopping rules, investigating how they influence overall session performance, and which rules best match actual stopping behaviour. Our work is focused on stopping at the query level in the context of ad-hoc topic retrieval, where searchers undertake search tasks within a fixed time period. We show that stopping strategies based upon the disgust or frustration point rules - both of which capture a searcher's tolerance to non-relevance - typically result in (i) the best overall performance, and (ii) provide the closest approximation to actual searcher behaviour, although a fixed depth approach also performs remarkably well. Findings from this study have implications regarding how we build measures, and how we conduct simulations of search behaviours.

CORE (COnnecting REpositories)