A personalised semantic and spatial information retrieval system based on user's modelling and accessibility measure

Search personalisation is a multi-criteria decision problem whose objective is to filter relevant information based on a set of criteria such as needs, interests and content semantics. Hereby, different users could enter the same query into a search system, but their information needs can be very different. Web personalisation can be seen as an interdisciplinary field whose objective is to facilitate the interaction between web content and users needs. It includes per definition several research domains from social to information sciences. The personalised search focuses on integrating users contexts, needs and relevancy criteria in the information retrieval process in order to help them finding the right content. This paper presents users network modelling for personalised spatial and semantic information retrieval. The idea is to provide a user with personalised results based on his model and on the neighbour users models. The spatial personalisation search is based on a measure of spatial accessibility, whose objective is to predict and evaluate location relevancy, accessibility and associations at the user level. This measure favours delivery of location-based and personalised recommendations. Our experiments confirm the effectiveness of our proposal by pointing out the improvement of the personalised search results when compared to a baseline web search.