TripBuilder: A Tool for Recommending Sightseeing Tours

Brilhante, Igo Ramalho; Macêdo, José Antônio Fernandes de; Nardini, Franco Maria; Perego, Raffaele; Renso, Chiara

doi:10.1007/978-3-319-06028-6_93

Cited by 27 publications

(27 citation statements)

References 4 publications

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“…In addition to the experimentation, we compare our approach to similar ones that are presented in [55,56]. The comparison is based on (i) input and output data of the recommendation engine and (ii) implemented algorithms.…”

Section: Comparison To Similar Approachesmentioning

confidence: 99%

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Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

Rajaonarivo

Fonteles

Sallaberry

et al. 2019

IJGI

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The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.

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Section: Comparison To Similar Approachesmentioning

confidence: 99%

“…Ref. [55] recommends POIs while [56] recommends touristic and leisure activities. In our approach, we recommend cultural heritage POIs.…”

Section: Comparison To Similar Approachesmentioning

confidence: 99%

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

Rajaonarivo

Fonteles

Sallaberry

et al. 2019

IJGI

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“…• Individual trip planning or collaborative planning: journey agenda and route planning (like in Travefy 11 and Tripomatic 12 ), transportation planning (like in RouteRank 13 and Hyperdia 14 ).…”

Section: A General Information Services For Travelersmentioning

confidence: 99%

“…TripBuilder framework described in [13] is an implementation of an approach for planning personalized sightseeing tours in cities. The itineraries are being constructed after analysis of the geo-tagged photos collected from Flickr 22 and associated with the POIs collected from Wikipedia.…”

Section: B Itinerary Construction: Interfaces and Implementationsmentioning

confidence: 99%

Information Management for Travelers: Towards Better Route and Leisure Suggestion

Pyshkin

Baratynskiy

Chisler

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Contemporary travel information services are connected to huge amount of travel related data used for improving personalized suggestions. Such suggestions include finding better routes, access to amusement and educational amenities implemented as digital services, as well as the features for people collaboration, and for planning leisure time with respect to existing attractiveness evaluation algorithms under time-budget constraints. Much effort is required for supporting personalized itineraries construction in such a way which would leverage existing cultural and technological user experience. In this paper we analyze the underlying algorithms and major components being an implementation of the proposed model investigated with particular attention to annotated leisure walk route construction, traveler collaboration and travel meeting management. In sum, we make an effort to address a number of complex issues in the area of developing models, interfaces and algorithms required by modern travel services considered as an essential application of a human-centric computing multidisciplinary paradigm.

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“…Then, the selected itineraries are joined in a sightseeing itinerary by means of a heuristic algorithm addressing the Trajectory Scheduling Problem (TRAJSP), a particular instance of Traveling Salesman Problem (TSP). The formalization of TRIPCOVER as an instance of the Generalized Maximum Coverage (GMC) problem can be found in Brilhante, Macedo, Nardini, Perego, and Renso (2013), while (Brilhante, Macedo, Nardini, Perego, & Renso, 2014) demos the capabilities of the TRIPBUILDER application. 5 In this paper, we extend the previous research with the following relevant and original contributions:…”

Section: Introductionmentioning

confidence: 99%