This thesis investigates the use of static, adaptive, adaptable and mixed-initiative approaches to the personalisation of content and graphical user interfaces (GUIs). This empirical study consisted of three experimental phases. The first examined the use of static, adaptive, adaptable and mixed-initiative approaches to web content. More specifically, it measured the usability (efficiency, frequency of error occurrence, effectiveness and satisfaction) of an e-commerce website. The experiment was conducted with 60 subjects and was tested empirically by four independent groups (15 subjects each). The second experiment examined the use of adaptive, adaptable and mixed-initiative approaches to GUIs. More specifically, it measured the usability (efficiency, frequency of error occurrence, effectiveness and satisfaction) in GUI control structures (menus). In addition, it investigated empirically the effects of content size on five different personalised menu types. In order to carry out this comparative investigation, two independent experiments were conducted, on small menus (17 items) and large ones (29 items) respectively. The experiment was conducted with 60 subjects and was tested empirically by four independent groups (15 subjects each). The third experiment was conducted with 40 subjects and was tested empirically by four dependent groups (5 subjects each). The aim of the third experiment was to mitigate the drawbacks of the adaptive, adaptable and mixedinitiative approaches, to improve their performance and to increase their usability by using multimodal auditory solutions (speech, earcons and auditory icons). The results indicate that the size of content affects the usability of personalised approaches. In other words, as the size of content increases, so does the need of the adaptive and mixed-initiative approaches, whereas that of the adaptable approach decreases. A set of empirically derived guidelines were also produced to assist designers with the use of adaptive, adaptable and mixed-initiative approaches to web content and GUI control structure.

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.