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Developing a context-aware electronic tourist guide: some issues and experiences

Authors:

Keith Cheverst,

Keith Mitchell,

Christos EfstratiouAuthors Info & Claims

CHI '00: Proceedings of the SIGCHI conference on Human Factors in Computing Systems

Pages 17 - 24

https://doi.org/10.1145/332040.332047

Published: 01 April 2000 Publication History

Abstract

In this paper, we describe our experiences of developing and evaluating GUIDE, an intelligent electronic tourist guide. The GUIDE system has been built to overcome many of the limitations of the traditional information and navigation tools available to city visitors. For example, group-based tours are inherently inflexible with fixed starting times and fixed durations and (like most guidebooks) are constrained by the need to satisfy the interests of the majority rather than the specific interests of individuals. Following a period of requirements capture, involving experts in the field of tourism, we developed and installed a system for use by visitors to Lancaster. The system combines mobile computing technologies with a wireless infrastructure to present city visitors with information tailored to both their personal and environmental contexts. In this paper we present an evaluation of GUIDE, focusing on the quality of the visitor's experience when using the system.

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Index Terms

Developing a context-aware electronic tourist guide: some issues and experiences

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cover image ACM Conferences

CHI '00: Proceedings of the SIGCHI conference on Human Factors in Computing Systems

April 2000

587 pages

ISBN:1581132166

DOI:10.1145/332040

Chairmen:
Thea Turner
Motorola Labs
,
Gerd Szwillus
Univ. of Paderborn, Paderborn, Germany

Copyright © 2000 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 April 2000

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April 1 - 6, 2000

The Hague, The Netherlands

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CHI '00 Paper Acceptance Rate 72 of 336 submissions, 21%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Karthiyayini JAnandhi R(2024)To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel RecommendationsSN Computer Science10.1007/s42979-024-02667-x5:4Online publication date: 27-Mar-2024
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https://doi.org/10.1002/9781394214167.ch23
Viswanathan SBoulard CBruyat AMaria Grasso A(2022)Situational Recommender: Are You On the Spot, Refining Plans, or Just Bored?Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501909(1-19)Online publication date: 29-Apr-2022
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