Mobile Tourist Guides Research Articles

An Experiment to Discover Usability Guidelines for Designing Mobile Tourist Apps

Tourist apps can be very useful for sightseeing, and this is one of the reasons that makes them so numerous in app stores. Good usability can make the difference when choosing an app, from the user’s point of view, so this study is aimed at analyzing and discovering common usability issues in apps for tourism. This paper presents a research study with two objectives: analyzing the most common usability problems in mobile apps for tourism and proposing recommendations for improving the usability of those apps. The research process firstly identifies the main functionalities that tourist guides should have, by filtering the results obtained through the store. This was followed by a two‐part experiment. The first part was a Keystroke Level Modelling analysis, where the number of taps needed to perform each main functionality in each tourist guide app was registered. Secondly, a heuristic evaluation was carried out on the best rated apps in the previous stage. Four usability experts tested the apps in real devices, by performing several tasks with a smartphone, and evaluated them according to some mobile heuristics. After this process, the most frequent usability problems in tourist guide apps were found, which allowed to discover recommendations for designing and developing mobile tourist guides. One of the recommendations is that tourist guide apps should provide at least the following functionalities: listing POIs (Points of Interest), showing information of a POI, selecting language, showing a map, working offline, and showing a tourist’s current location. Other recommendations proposed are, for example, showing relevant and complete information of POIs, such as opening hours and admission fees; showing more than one POI on the map and the distance to the tourist; and avoiding showing at the same time commercial information and tourist information.

Design and Implementation of an Optimal Travel Route Recommender System on Big Data for Tourists in Jeju

A recommender system is currently applied in many different domains, seeking to provide users with recommendation services according to their personalized preferences to relieve rising online information congestion. As the number of mobile phone users is large and growing, mobile tourist guides have attracted considerable research interest in recent years. In this paper, we propose an optimal travel route recommender system by analyzing the data history of previous users. The open dataset used covers the travel data from thousands of mobile tourists who visited Jeju in a full year. Our approach is not only personalized to users’ preferences but also able to recommend a travel route rather than individual POIs (Points of Interest). An association rule mining-based approach, which takes into account contextual information (date, season and places already visited by previous users), is used to produce travel routes from the large dataset. Furthermore, to ensure the reasonability of the recommendation, a genetic algorithm optimization approach is proposed to find the optimal route among them. Finally, a mobile tourist case study is implemented in order to verify the feasibility and applicability of the proposed system. This application embeds a graphic map for plotting the travel route and provides detailed information of each travel spot as well. The results of this work indicate that the proposed system has great potential for travel planning preparation for mobile users.

A web-based pervasive recommendation system for mobile tourist guides

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user's location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of `context-aware rating', whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.

Electronic mobile guides: a survey

Mobile tourist guides have been in the spot light for the past decade and are becoming increasingly available in various forms to tourists visiting places. The majority of these mobile tourist guides are to be used via a constant network connection and some as proprietary standalone mobile applications installed on-device. Some are solely navigational assistants using positioning technologies for large cities offering exploratory services and others are used indoors, for example as museum guides. This research paper attempts to categorize these mobile guides using a detailed set of evaluation criteria in order to extract design principles which can be used by application designers and developers.

A PERSONALIZED TOURIST TRIP DESIGN ALGORITHM FOR MOBILE TOURIST GUIDES

Mobile tourist guides evolve towards automated personalized tour planning devices. The contribution of this article is to put forward a combined artificial intelligence and metaheuristic approach to solve tourist trip design problems (TTDP). The approach enables fast decision support for tourists on small footprint mobile devices. The orienteering problem, which originates in the operational research literature, is used as a starting point for modelling the TTDP. The problem involves a set of possible locations having a score and the objective is to maximize the total score of the visited locations, while keeping the total time (or distance) below the available time budget. The score of a location represents the interest of a tourist in that location. Scores are calculated using the vector space model, which is a well-known technique from the field of information retrieval. The TTDP is solved using a guided local search metaheuristic. In order to compare the performance of this approach with an algorithm that appeared in the literature, both are applied to a real data set from the city of Ghent. A collection of tourist points of interest with descriptions was indexed and subsequently queried with popular interests, which resulted in a test set of TTDPs. The approach presented in this article turns out to be faster and produces solutions of better quality.

Information Search with Mobile Tourist Guides: A Survey of Usage Intention

As mobile tourist guides show huge potentials to support tourists’ information search, executives need to know which mobile systems characterized by which specific functionalities should be provided. The paper proposes an empirical approach combining qualitative and quantitative methods to deduce both, product definition and product marketing information related to mobile tourist guides, respectively. First, a methodology-mix combining qualitative (i.e. explorative survey, focus group interviews) and quantitative techniques (i.e. pilot study, expert interviews) responds to the question ‘Which services should be developed?’ Secondly, again in the pre-prototype phase and based on survey data (N = 705) ideal functionality compositions for promising tourist guides are deduced with regression analysis. Finally, based on technology acceptance theory (Venkatesh et al., 2003a) customer prejudices towards mobile tourist guides are identified. The obtained results support hypotheses indicating strong performance expectations and pronounced effort expectancies towards the deduced concepts designed to support information search by means of mobile tourist guides

Toward a Multidisciplinary Model of Context to Support Context-Aware Computing

Capturing, defining, and modeling the essence of context are challenging, compelling, and prominent issues for interdisciplinary research and discussion. The roots of its emergence lie in the inconsistencies and ambivalent definitions across and within different research specializations (e.g., philosophy, psychology, pragmatics, linguistics, computer science, and artificial intelligence). Within the area of computer science, the advent of mobile context-aware computing has stimulated broad and contrasting interpretations due to the shift from traditional static desktop computing to heterogeneous mobile environments. This transition poses many challenging, complex, and largely unanswered research issues relating to contextual interactions and usability. To address those issues, many researchers strongly encourage a multidisciplinary approach. The primary aim of this article is to review and unify theories of context within linguistics, computer science, and psychology. Summary models within each discipline are used to propose an outline and detailed multidisciplinary model of context involving (a) the differentiation of focal and contextual aspects of the user and application's world, (b) the separation of meaningful and incidental dimensions, and (c) important user and application processes. The models provide an important foundation in which complex mobile scenarios can be conceptualized and key human and social issues can be identified. The models were then applied to different applications of context-aware computing involving user communities and mobile tourist guides. The authors' future work involves developing a user-centered multidisciplinary design framework (based on their proposed models). This will be used to design a large-scale user study investigating the usability issues of a context-aware mobile computing navigation aid for visually impaired people.