A Constraint Inductive Learning-Spectral Clustering Methodology for Personalized 3D Navigation

Abstract

The recent advances in ICT boost research towards the generation of personalized Geographical Information Systems (p-GIS). It is clear that selection of a route based only on geometrical criteria, i.e., the route of the shortest distance or the minimum travel time, very rarely coincides with a “satisfactory itinerary” that respects users’ preferences, that is their desires to navigate through buildings or places of his/her own particular interest. Additionally, 3D navigation gains more popularity compared with 2D approaches especially in virtual tourist and cultural heritage applications. In a p-GIS, user’s preferences can be set manually or automatically. In an automatic architecture, user preferences are expressed as a set of weights that regulate the degree of importance on the route selection process and on line learning strategies are exploited to adjust the weights. In this paper, the on-line learning strategy exploits information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Then, we use a constraint fusion methodology for the dynamic modeling of user’s preference in a 3D navigation system. The method exploits an active inductive learning approach that is combined with an adaptive spectral clustering scheme in order to avoid smoothing during the weight adjustment process.