In this paper we present two design guidelines, causal order and continuity, to be used as rules of thumb for designing intuitive interactions based on principles of causal reasoning. We propose that designing interactions to behave like real-world systems of cause and effect makes them more intuitive. Using these basic principles avoids the limitations inherent to specific metaphors. In three experiments, participants solved puzzles using variations of a novel graphical interface. Participants using interfaces that were consistent with the causal guidelines consistently solved the puzzle faster than participants using inconsistent interfaces. We also discuss common interactions already consistent with the causal guidelines as well as areas where the guidelines are likely to apply successfully. The causal order guidelines provide specific utility while also demonstrating how principles of causal psychology can be applied to help interface designers better convey the functionality of their interfaces.

This work tries to answer the question of what makes a query difficult. It addresses a novel model that captures the main components of a topic and the relationship between those components and topic difficulty. The three components of a topic are the textual expression describing the information need (the query or queries), the set of documents relevant to the topic (the Qrels), and the entire collection of documents. We show experimentally that topic difficulty strongly depends on the distances between these components. In the absence of knowledge about one of the model components, the model is still useful by approximating the missing component based on the other components. We demonstrate the applicability of the difficulty model for several uses such as predicting query difficulty, predicting the number of topic aspects expected to be covered by the search results, and analyzing the findability of a specific domain.

In this article we present novel learning methods for estimating the quality of results returned by a search engine in response to a query. Estimation is based on the agreement between the top results of the full query and the top results of its sub-queries. We demonstrate the usefulness of quality estimation for several applications, among them improvement of retrieval, detecting queries for which no relevant content exists in the document collection, and distributed information retrieval. Experiments on TREC data demonstrate the robustness and the effectiveness of our learning algorithms.