Tip: Personalizing Information Delivery in a Tourist Information System

Hinze, Annika; Voisard, Agnès; Buchanan, George

doi:10.3727/109830509x12596187864071

Cited by 24 publications

(12 citation statements)

References 2 publications

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“…Model-based approaches to software design, particularly for mobile or multi-platform software are increasing in popularity and may take a transformational approach as in such work as [9,11,14] for example, or a more test-driven approach as in [10]. While the work we describe here is primarily focused on interaction of mobile services and how we can use models of behaviour and interfaces to support development of such applications, we are particularly interested in work such as in [6] which takes a similar approach to ours but from the perspective of contextual considerations for interfaces of mobile devices.…”

Section: Discussionmentioning

confidence: 99%

“…We incorporated selected formal constraints for the user interface in an automatic way by extending existing formal models for user interface designs. We tested the emulator on the design of a tourist information system TIP [9], which presents contextsensitive information to mobile users with small screen devices. The dynamics of this system are very complex and pose several challenges, firstly because of the sophisticated interaction of the user with several applications on a small screen device, and secondly because of the need for communication with highly asynchronous event-based information systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-driven GUI & interaction design using emulation

Hinze

Bowen

Wang

et al. 2010

Proceedings of the 2nd ACM SIGCHI Symposium on Engineering Interactive Computing Systems

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-driven GUI & interaction design using emulation

Hinze

Bowen

Wang

et al. 2010

Proceedings of the 2nd ACM SIGCHI Symposium on Engineering Interactive Computing Systems

Self Cite

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“…There also exist a recommendation system based on text mining techniques between a travel agent and a customer through a private Web chat [16]. A personalized tourist information provider is introduced as a combination of an eventbased system and a location-based service applied to a mobile environment in [4], [11].…”

Section: Related Workmentioning

confidence: 99%

MARST: Multi-Agent Recommender System for e-Tourism Using Reputation Based Collaborative Filtering

Bedi

Agarwal

Jindal

et al. 2014

Databases in Networked Information Systems

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Abstract. This paper presents a Multi-Agent Recommender system for eTourism (MARST) for recommending tourism services to the users. This system uses Reputation based Collaborative Filtering (RbCF) algorithm that augments reputation to existing Collaborative approach for generating relevant recommendations and to handle cold-start new user problem in tourism domain. The structure of a tourist product is more complex than a book or a movie and hence user profile modeling for these systems is much harder than most of other applications domains like books or movies. Moreover the frequency of activities and rating in tourism domain is also much smaller than in most of the other domains. This increases the complexity in designing and development of Recommender Systems in tourism domain. An attempt has been made in this paper to generate relevant services for a user in tourism domain using reputation based collaborative filtering. Most of the existing Recommender systems focus on one service at a time, whereas the proposed system incorporates three services (hotels, places to visit and restaurants) at a single place to ease the searching of information at one place only. The prototype of MARST has been designed and developed using various JAVA technologies and its performance was evaluated using precision, recall and F 1 metrics.

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“…These points are taken by the system as semantic groups and the active user is classified, according to the data in his profile, in one of these groups. TIP is a combination of an event-based system (EBS) and a location-based service (LBS) applied to a mobile environment (Hinze, Voisard, & Buchanan, 2009). …”

Section: Tourism Recommender Systemsmentioning

confidence: 99%

A hybrid recommendation approach for a tourism system

Lucas

Luz

Moreno

et al. 2013

Expert Systems with Applications

156

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A B S T R A C TMany current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulner-able to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.K e y w o r d s : Recommender systems, Associative classification, Fuzzy logic

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Tip: Personalizing Information Delivery in a Tourist Information System

Cited by 24 publications

References 2 publications

Model-driven GUI & interaction design using emulation

Model-driven GUI & interaction design using emulation

MARST: Multi-Agent Recommender System for e-Tourism Using Reputation Based Collaborative Filtering

A hybrid recommendation approach for a tourism system

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