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The formal testing of IR systems involves setting up an experimental corpus (test collection, indexing, test questions etc.) and measuring performance under specific experi~ental conditions. However, the object of any IR test is to make a prediction, extrapolating from the test conditions, about a different situation. Up to now, this extrapolation has been made on the assumption V1at the relative performance of different systems is independent of the situation; but in some recent reported experiments, the assumption has been found not to hold. A more formal theory, indicating how the performance of a system varies according to the conditions, is therefore· needed. Elements of such a theory are proposed and discussed. Particular areas identified as being of concern to such a theory are: the nature of relevance, the various possible forms of the Sw·ets 'control variable', and the psychology of the searching process. The basic quantitative concept used in the theory is the probability, for each question, that the system Vlill retrieve a document of a given de~ree of relevance to that question. Present methods of estimating these probabilities appear to be inadequate; a new method based on a Bayesian approa.ch is developed. The method involves a prior assumption about the distribution of these probabilities over different questions, and makes use of this distribution in estimating them.~ The new method is tested on a variety of sets of data from published tests. The results appear satisfactory, and in some cases suggest new ways of looking at the data. They also indicate that some quantitative aspects of the theory will need modification. Further developments of the theory include: an indication of the possible uses of simulation models, and an application of the theory to the problem of how to make best use of relevance feedback data.

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The study of information-seeking behaviour of scientists has been one of the main concerns of librarians and information scientists since mid twentieth century and yet we need to improve our understanding of their information behaviour in order to maximise the efficiency of information services provided. This thesis studies the information-seeking behaviour of physicists and astronomers with an intradisciplinary approach in order to look at similarities and dissimilarities among the subfields within physics and astronomy. The study also looks at the information-seeking behaviour of people with different academic status and investigates the information-seeking activities of physicists and astronomers in different stages of research projects with the focus of the thesis being research related information-seeking behaviour. Moreover, the research investigates reading behaviour and publishing patterns of physicists and astronomers. The study is a mixed-methods study that uses both qualitative and quantitative approaches. The population of the study included the staff and PhD students in the Department of Physics and Astronomy of University College London. Fifty-six face-to-face semi-structured interviews were conducted, an online questionnaire survey of 114 respondents (out of 242 sample, 47% response rate) was carried out and 88 information-event cards were completed by participants. The findings of the study showed that although some similarities exist in information-seeking behaviour of people in the different subfields of physics and astronomy, each subfield has its own characteristics. Variations were found with regard to different aspects of information-seeking behaviour including the reliance on e-print archives and journal articles, methods used for keeping up-to-date and methods used for identifying articles. The study showed the importance of human information sources and informal communication in the information-seeking behaviour of physicists and astronomers and highlighted the need for and the value of looking at narrower subject communities within disciplines for a deeper understanding of the information behaviour of scientists.

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The research set out with the broad objective of developing new tools to support Web information searching. A survey showed that a substantial number of interactive search tools were being developed but little work on how these new developments fitted into the general aim of helping people find information. Due to this it proved difficult to compare and analyse how tools help and affect users and where they belong in a general scheme of information search tools. A key reason for a lack of better information searching tools was identified in the ill-suited nature of existing information retrieval system models. The traditional information retrieval model is extended by synthesising work in information retrieval and information seeking research. The purpose of this new holistic search model is to assist information system practitioners in identifying, hypothesising, designing and evaluating Web information searching tools. Using the model, a term relevance feedback tool called 'Tag and Keyword' (TKy) was developed in a Web browser and it was hypothesised that it could improve query reformulation and reduce unnecessary browsing. The tool was laboratory experimented and quantitative analysis showed statistical significances in increased query reformulations and in reduced Web browsing (per query). Subjects were interviewed after the experiment and qualitative analysis revealed that they found the tool useful and saved time. Interestingly, exploratory analysis on collected data identified three different methods in which subjects had utilised the TKy tool. The research developed a holistic search model for Web searching and demonstrated that it can be used to hypothesise, design and evaluate information searching tools. Information system practitioners using it can better understand the context in which their search tools are developed and how these relate to users' search processes and other search tools.

Due to advances in computer technology, large image collections have been digitised and archived in computers. Image management systems are therefore developed to retrieve relevant images. Because of the limitations of text-based image retrieval systems, Content-Based Image Retrieval (CBIR) systems have been developed. A CBIR system usually extracts global or local contents of colour, shape and texture from an image to form a feature vector that is used to index the image. Plethora methods have been developed to extract these features, however, there is very little in the literature to study the closeness of each method to human perception. This research aims to develop a human perception oriented content-based image retrieval system for the Museum of Domestic Design & Architecture (MoDA) wallpaper images. Since texture has been widely regarded as the main feature for these images and applied in CBIR systems, psychophysical experiments were conducted to study the way human perceive texture and to evaluate five popular computational models for texture representations: Grey Level Co-occurrence Matrices (GLCM), Multi-Resolution Simultaneous Auto-Regressive (MRSAR) model, Fourier Transform (FT), Wavelet Transform (WT) and Gabor Transform (GT). By analyzing experimental results, it was found that people consider directionality and regularity to be more important in terms of texture than coarseness. Unexpectedly, none of the five models appeared to represent human perception of texture very well. It was therefore concluded that classification is needed before retrieval in order to improve retrieval performance and a new classification algorithm based on directionality and regularity for wallpaper images was developed. The experimental result showed that the evaluation algorithm worked effectively and the evaluation experiments confirmed the necessity of the classification step in the development of CBIR system for MoDA collections.

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