Human-based query difficulty prediction

The purpose of an automatic query difficulty predictor is to decide whether an information retrieval system is able to provide the most appropriate answer for a current query. Researchers have investigated many types of automatic query difficulty predictors. These are mostly related to how search engines process queries and documents: they are based on the inner workings of searching/ranking system functions, and therefore they do not provide any really insightful explanation as to the reasons for the difficulty, and they neglect user-oriented aspects. In this paper we study if humans can provide useful explanations, or reasons, of why they think a query will be easy or difficult for a search engine. We run two experiments with variations in the TREC reference collection, the amount of information available about the query, and the method of annotation generation. We examine the correlation between the human prediction, the reasons they provide, the automatic prediction, and the actual system effectiveness. The main findings of this study are twofold. First, we confirm the result of previous studies stating that human predictions correlate only weakly with system effectiveness. Second, and probably more important, after analyzing the reasons given by the annotators we find that: (i) overall, the reasons seem coherent, sensible, and informative; (ii) humans have an accurate picture of some query or term characteristics; and (iii) yet, they cannot reliably predict system/query difficulty. © Springer International Publishing AG 2017.