Neural Network based Approach for Predicting user Satisfaction with Search Engine

Abstract

Success of a search engine is measured by the satisfaction of its users. Finding user expectation can be a better step for improved user satisfaction. In this paper we have proposed a neural network based approach for predicting user satisfaction with search engine. Our work is divided in two parts. Part I investigates user expectations towards search engine for their information need. In Part II we proposed an Artificial Neural Network (ANN) model for predicting User Satisfaction. In our work we have analyzed the major factors affecting user satisfaction with search engine and find out the importance /priority value of these factors based on a survey conducted on 100 search engine users of different profiles with 5-10 years of experience using search engines for their information needs like study material, entertainment, research, day to day problem solution etc. In the present work we have identified four major factors namely up-to-date information, search result relevancy, response time and reliability, contributing to the user satisfaction and developed an ANN model which predicts satisfaction results with a reasonable degree of accuracy.

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

“…Artificial neural networks (ANNs) have a remarkable ability to learn any linear or non-linear function from input and output data. Therefore, they are widely used in domains of search engines [26], power systems [27], transportation [28], agriculture [29], meteorology [30], and so on. In this article, we use the artificial neural network to learn the optimal functions from the knowledge of experts and combine the elementary similarities of selected characteristics to the overall similarity of geospatial data, aiming to improve the precision of the similarity measures of geospatial data.…”