A Flexible Competitive Neural Network for Eliciting User’s Preferences in Web Urban Spaces

Preference elicitation is a non-deterministic process that involves many intuitive and non well-defined criteria that are difficult to model. This paper introduces a novel approach that combines image schemata, affordance concepts and neural network for the elicitation of user’s preferences within a web urban space. The selection parts of the neural network algorithms are achieved by a web-based interface that exhibits image schemata of some places of interest. A neural network is encoded and decoded using a combination of semantic and spatial criteria. The semantic descriptions of the places of interest are defined by degrees of membership to predefined classes. The spatial component considers contextual distances between places and reference locations. Reference locations are possible locations from where the user can act in the city. The decoding part of the neural network algorithms ranks and evaluates reference locations according to user’s preferences. The approach is illustrated by a web-based interface applied to the city of Kyot