Intelligent systems for tourism

Stabb, Sally D.; Werther, H.; Ricci, Francesco; Zipf, Alexander; Gretzel, Ulrike; Fesenmaier, Daniel R.; Paris, Cécile; Knoblock, Craig A.

doi:10.1109/mis.2002.1134362

Cited by 132 publications

(56 citation statements)

References 19 publications

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“…Meanwhile, several works exist that discuss personalized information recommendation services, which have been applied into various contexts such as job search (Al-Otaibi, 2012;Malinowski, 2006;Paparrizos, 2011), expert finding (Cao, 2005;Fang, 2007;Macdonald, 2008), travel planning (Park, 2008;Staab, 2002;Yu, 2009), and restaurant recommendation (Lee, 2006;Liang, 2008;Mui, 2001). For example, Al-Otaibi and Ykhelf (2012) built recommender systems to provide job information for job hunters.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Find a Comfortable Bus Route - Towards Personalized Information Recommendation Services

Guo¹,

Zhao²,

Xu³

et al. 2015

CODATA

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With rapid economic growth, city sizes are expanding, and city traffic flow is surging. Thus traffic congestion, traffic accidents, and serious air pollution are becoming more common. Priority has been given to the development of urban public transportation policy by using GPS, GIS, the internet, and communication technology to realize the collection, transmission, storage, and processing of the massive historical and real-time data gathered from bus IC cards, in order to build an intelligent, modern city public transport dispatching platform that will solve social problems such as urban traffic congestion, energy shortages, and air pollution. In addition, when passengers choose a reasonable travel plan, they can lessen not only their travel costs but can also increase the efficiency of public transport vehicles. Therefore, the choice of a reasonable bus route is of great importance to the daily operation and management of urban traffic.In recent years both in China and abroad, urban public transport has been the subject of extensive research and discussion. However, there are still many complaints about crowded buses, the length of waiting time, and the unpunctuality of the buses. How to effectively and efficiently recommend comfortable bus routes to bus passengers is a challenging and complex task. To address this critical challenge, we must consider factors reflecting the passengers' demands such as waiting time, crowded time, and driving time. To recommend comfortable bus routes for bus passengers, we suggest using multi-objective programming with various constraints and have developed a genetic algorithm to search for solutions. As a result, a bus route according to the differing requirements of passengers can be recommended.The rest of the paper is organized as follows. In Section 2, we review related work on the data processing of bus IC cards and personalized information recommendation services. In Section 3, we present a multiobjective program with various constraints to recommend comfortable bus routes for bus passengers and use a genetic algorithm to search for solutions. In Section 4, we discuss our implementation and empirical analysis of the proposed method. Finally, we conclude the paper and point out future research directions. With the generation of massive data from bus IC cards, how to effectively and efficiently recommend comfortable bus routes to bus passengers is a challenging and complex task. In this paper, waiting time, crowded time, and driving time between different bus stations on different bus routes at different times of the day are calculated from bus IC cards data history. Then, a multiobjective program with various constraints is suggested to recommend comfortable bus routes for bus passengers, and a genetic algorithm is developed to search for expected solutions. The proposed method is implemented using bus IC cards data from Chongqing, China and will be a promising tool for bus passengers when choosing comfortable bus routes. PROCEEDINGS PAPER

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Section: Literature Reviewmentioning

confidence: 99%

How to Find a Comfortable Bus Route - Towards Personalized Information Recommendation Services

Guo¹,

Zhao²,

Xu³

et al. 2015

CODATA

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“…Finally, the travel assistant domain used as a testbed in this paper, has been widely used in the literature [2, 11,22,24,28,33].…”

Section: Related Workmentioning

confidence: 99%

Multi-agent plan based information gathering

et al. 2006

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The evolution of the Web has encouraged the development of new Information Gathering techniques. Artificial Intelligence techniques, such as Planning, have also been used for Information Gathering in order to go beyond merely retrieving Web data. Planning has been used traditionally to generate a sequence of actions that specify how information sources should be accessed. In this paper, planning is used mainly for integrating information found in heterogeneous sources. For instance, two different Web sources about flight and train travels, can be represented by two different planning operators, which will be subsequently combined and integrated by a single plan. We have found that a Multi-Agent framework is very appropriate to implement our technique. In order to evaluate our approach empirically, it has been applied to a tourism domain (MAPWEB-ETOURISM), whose purpose is to help a customer to plan his/her trips. In this domain, several specialized Web agents have been used to query travel Web sources, whose results are subsequently integrated by a planning agent to build complete travel solutions. Experimental results show that, by means of integration, more solutions can be found than by using single information sources or even travel meta-searchers. Also, MAPWEB-ETOURISM can find new types of solutions by integrating information gathered from heterogeneous Web sources (i.e. flights and trains).

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“…Such suggestions are produced based on inputs, and a simple approach would be to ask the user about his or her preferences and to include information, such as user needs, interests and constraints, enter those into some system and then have the system correlate these preferences with POIs that have similar parameters [2]. Another option is to use additional input evaluations and ratings of other tourists with similar interests in a collaborative filtering fashion [3]. Additionally, travelers who are in close spatial and temporal proximity often share common travel interests or needs in a crowd-sourced manner [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Privacy-by-Design Contextual Suggestion System for Tourism

Efraimidis

Drosatos

Arampatzis

et al. 2016

JSAN

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Abstract:We focus on personal data generated by the sensors and through the everyday usage of smart devices and take advantage of these data to build a non-invasive contextual suggestion system for tourism. The system, which we call Pythia, exploits the computational capabilities of modern smart devices to offer high quality personalized POI (point of interest) recommendations. To protect user privacy, we apply a privacy by design approach within all of the steps of creating Pythia. The outcome is a system that comprises important architectural and operational innovations. The system is designed to process sensitive personal data, such as location traces, browsing history and web searches (query logs), to automatically infer user preferences and build corresponding POI-based user profiles. These profiles are then used by a contextual suggestion engine to anticipate user choices and make POI recommendations for tourists. Privacy leaks are minimized by implementing an important part of the system functionality at the user side, either as a mobile app or as a client-side web application, and by taking additional precautions, like data generalization, wherever necessary. As a proof of concept, we present a prototype that implements the aforementioned mechanisms on the Android platform accompanied with certain web applications. Even though the current prototype focuses only on location data, the results from the evaluation of the contextual suggestion algorithms and the user experience feedback from volunteers who used the prototype are very positive.

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Intelligent systems for tourism

Cited by 132 publications

References 19 publications

How to Find a Comfortable Bus Route - Towards Personalized Information Recommendation Services

How to Find a Comfortable Bus Route - Towards Personalized Information Recommendation Services

Multi-agent plan based information gathering

A Privacy-by-Design Contextual Suggestion System for Tourism

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