User Modelling for Interactive Optimization Using Neural Network

User modelling is one of the prominent research fields in information retrieval systems. In this paper, we model user's preferences and search criteria using an NN (Neural Network) to solve a multiobjective optimization problem specific to environmental planning systems. We argue that some NP hard problems cannot be solved alone either by a human or by a computer. Human participation in automated search is one way of combining human intuition with algorithmic search to solve such problems. However, even humans have some limitations for participation in that they cannot participate in search completely because of human fatigue. To overcome this, in our approach, an NN tries to model the user's rating criteria and preferences to help the user in rating large set of designs. Although training an NN with limited data is not always feasible, there are many situations where a simple modelling technique (e.g., linear/quadratic mapping) works better if the learning data set is small. In this paper we attempt to get more accuracy of the NN by generating data using other linear/non-linear techniques that fills the gap created by lack of sufficient training data. Also, we provided the architectural design of an HPC based framework we have proposed and compared the performance of the NN with fuzzy logic and other linear/non-linear user modelling techniques for the environmental resources optimization problem.