Providing personalised information based on individual interests and preferences.

The main aim of personalised Information Retrieval (IR) is to provide an effective IR system whereby relevant information can be presented according to individual users' interests and preferences. In response to their queries, all Web users expect to obtain the search results in a rank order with the most relevant items at the lowest ranks. Effective IR systems rank the less relevant documents below the relevant documents. However, a commonly stated problem of Web browsers is to match the users' queries to the information base. The key challenge is to return a list of search results containing a low level of non-relevant documents while not missing out the relevant documents.To address this problem, keyword-based search of Vector Space Model is employed as an IR technique to model the Web users and build their interest profiles. Semantic-based search through Ontology is further employed to represent documents matching the users' needs without being directly contained in the users' specified keywords. The users' log files are one of the most important sources from which implicit feedback is detected through their profiles. These provide valuable information based on which alternative learning approaches (i.e. dwell-based search) can be incorporated into the IR standard measures (i.e. tf-idf) allowing a further improvement of personalisation of Web document search, thus increasing the performance of IR systems.To incorporate such a non-textual data type (i.e. dwell) into the hybridisation of the keyword-based and semantic-based searches entails a complex interaction of information attributes in the index structure. A dwell-based filter called dwell-tf-ldf that allows a standard tokeniser to be converted into a keyword tokeniser is thus proposed. The proposed filter uses an efficient hybrid indexing technique to bring textual and non-textual data types under one umbrella, thus making a move beyond simple keyword matching to improve future retrieval applications for web browsers. Adopting precision and recall, the most common evaluation measure, the superiority of the hybridisation of these approaches lies in pushing significantly relevant documents to the top of the ranked lists, as compared to any traditional search system. The results were empirically confirmed through human subjects who conducted several real-life Web searches.