Kenneth Treharne

Search Engine Result Visualisation: Challenges and Opportunities

The rank-ordered list format is the prevailing result presentation format of all the major Internet search engine providers. The format itself has seen few changes over the course of the Internet’s history aside from the emergence of the snippet. However, there are indications that the result presentation format is undergoing some evolutionary changes. The discussion will examine primarily the structural aspects of alternative formats to the traditional list format and motivate a program of comparative evaluation. A summary of current structure templates provides a resource for designers of future result visualizations.

A lean online approach to human factors research

Arranging sufficient research participation within time and resource constraints is seldom an easy feat. Often, such constraints are a detriment to thorough empirical evaluation of human-computer interfaces. Our objective is to develop the means to test and construct human models which when applied to interface and system development, result in efficient human computer interactions. We report an online platform for experiment delivery to diverse locations with an emphasis on alternative non-monetary rewards for participation. Such a platform meets research goals within constraints as it necessitates a simple web server and web programming. Also addressed are the omnipotent issues of ethics, privacy and data integrity that arise in similar proposals.

User keyword preference: the Nwords and Rwords experiments

Everyday, millions of people use some form of text-based interface to search inefficiently for information. This reflects a lack of penetration of key developments in Human Computer Interaction (HCI) designed to expedite document retrieval. In the context of document search, the value of textual language is self-evident for searching natural language documents. However, there has been little human factors research into general user preferences such as the number of words preferred to describe or search for a document. Such heuristics can help to balance processing overheads of tasks like clustering against user cognitive load in the realisation of, overall, more efficient document retrieval process. We report the results from two internet delivered experiments designed to obtain this heuristic information.

Interfaces for discourse summarisation: a human factors analysis

Empirical studies assessing the effectiveness of novel document interfaces are becoming more prevalent, however relatively little attention has been paid to how such tools could work with less structured documents featuring multiple contributors. Participants in this study used different interfaces to answer questions requiring the exploration of collaborative discourse. User performance was influenced by an interaction of interface, transcript, and question type. Individual differences also impacted on performance with higher education levels and higher general knowledge scores being associated with better task performance. The results also revealed that unnecessary interface functionality can hinder performance.

Magician simulator — A realistic simulator for heterogeneous teams of autonomous robots

We report on the development of a new simulation environment for use in Multi-Robot Learning, Swarm Robotics, Robot Teaming, Human Factors and Operator Training. The simulator provides a realistic environment for examining methods for localization and navigation, sensor analysis, object identification and tracking, as well as strategy development, interface refinement and operator training (based on various degrees of heterogeneity, robot teaming, and connectivity). The simulation additionally incorporates real-time human-robot interaction and allows hybrid operation with a mix of simulated and real robots and sensor inputs.

Using Semantic Context to Rank the Results of Keyword Search

ABSTRACT In an empirical user study, we assessed two approaches to ranking the results from a keyword search using semantic contextual match based on Latent Semantic Analysis. These techniques involved searches initiated from words found in a seed document within a corpus. The first approach used the sentence around the search query in the document as context while the second used the entire document. With a corpus of 20,000 documents and a small proportion of relevant documents (<0.1%), both techniques outperformed a conventional keyword search on a recall-based information retrieval (IR) task. These context-based techniques were associated with a reduction in the number of searches conducted, an increase in users’ precision and, to a lesser extent, an increase in recall. This improvement was strongest when the ranking was based on document, rather than sentence, context. Individuals were more effective on the IR task when the lists returned by the techniques were ranked better. User performance on the task also correlated with achievement on a generalized IQ test but not on a linguistic ability test.

Optimising visual and textual in search user interfaces

Improvements to the user interface of our search tools will play a prominent role toward improving search outcomes in the future. Three such improvements are proposed and evaluated in an exploratory investigation. This is a work in progress and at present, the reported results are preliminary. Nevertheless, significant findings indicate that the way the interface is configured can result in searchers adopting different interaction strategies. The main goals of this research have been to optimise visual and textual information presented on search user interfaces.