Application for e-Tourism: Intelligent Mobile Tourist Guide

Author(s):  
Alexander Smirnov ◽  
Alexey Kashevnik ◽  
Andrew Ponomarev ◽  
Maksim Shchekotov ◽  
Kirill Kulakov
Keyword(s):  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shun Li ◽  
Quan Xiao

An increasing number of people are using mobile applications to obtain travel-related information and activities due to the prosperity of the internet and mobile technologies nowadays. The design, development, and improvement of mobile tourist guide application (MTGA) are particularly important for travel-related companies. As an emerging application scenario of mobile technologies in the field of tourism, existing research on MTGA lacks analysis of its specific design, especially from the perspective of users to investigate the microscopic design features and improvement strategies. The Kano model was adopted by prior studies to analyse product quality attribute, while importance-performance analysis (IPA) was used to prioritize quality attributes for improvement. However, due to the limitation of the Kano model in neglecting the attribute performance and importance and the weakness of IPA in considering only the one-dimensional quality attributes, the use of single approach has its shortcomings for analysing the design features of MTGA. We attempt to integrate Kano model and IPA to conduct a study on the classification and improvement strategy issues for the design features of MTGA. Particularly, we identify design features of MTGA first, propose a method to classify them, and determine their priorities for developing and improving as well. An online questionnaire survey is conducted. The paper extends research on Kano model and IPA into the domain of mobile application design and provides insights into management strategies about the design of MTGA, which also offers novel and important implications for travel-related companies to increase the users’ satisfaction by optimizing their mobile application design.


2009 ◽  
Vol 14 (4) ◽  
pp. 483-502 ◽  
Author(s):  
Joerg Rasinger ◽  
Matthias Fuchs ◽  
Thomas Beer ◽  
Wolfram Höpken

Author(s):  
Wolfgang Woerndl ◽  
Korbinian Moegele ◽  
Vivian Prinz

This chapter presents an approach to extend a real world mobile tourist guide running on personal digital assistants (PDAs) with collaborative filtering. The system builds a model of item similarities based on explicit and implicit ratings. This model is then utilized to generate recommendations in several ways. The approach integrates the current user location as context. Experiences gained in two field studies are reported. In the first one, 30 participants – real tourists visiting Prague – used the recommender function and were asked to fill out a questionnaire with promising results. In a second field study analyzing usage log files, an improvement of recommendations based on the collaborative filter in comparison to the pure location-based filter used before was discovered. In addition, recommendations based on implicit ratings derived from audio playback duration outperformed the model based on explicit ratings.


Author(s):  
Juan Pavón ◽  
Juan M. Corchado ◽  
Jorge J. Gómez-Sanz ◽  
Luis F. Castillo Ossa

Author(s):  
Alexander Smirnov ◽  
Alexey Kashevnik ◽  
Sergey I. Balandin ◽  
Santa Laizane
Keyword(s):  

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