On planning sightseeing tours with TripBuilder

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

doi:10.1016/j.ipm.2014.10.003

Cited by 106 publications

(55 citation statements)

References 15 publications

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“…It has been extended by Lim et al [13], to mine social geotagged images' metadata and estimate users' preferences with timebased interests. A similar idea was considered in [14], which estimates users' preferences by the categories of the POIs. With the development of computer vision technology, an image feature-enhanced tour recommendation method was proposed in [15], to find POIs that are visually similar to users' uploaded images.…”

Section: Tour Recommendationmentioning

confidence: 99%

User Transition Pattern Analysis for Travel Route Recommendation

Sun

Zhuang

2019

IEICE Trans. Inf. & Syst.

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A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.

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Section: Tour Recommendationmentioning

confidence: 99%

User Transition Pattern Analysis for Travel Route Recommendation

Sun

Zhuang

2019

IEICE Trans. Inf. & Syst.

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“…The reasoning process also combines contentbased and collaborative recommendations. TRIPBUILDER [17] suggests personalized city sightseeing tours. POIs are derived from Wikipedia and illustrated by Flickr georeferenced photos.…”

Section: Context-aware Recommender Systems In the Tourismmentioning

confidence: 99%

“…However, and to the best of our knowledge, previous context-aware tourism recommender systems developed so far have not completely addressed these issues [9,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. One weakness of these researches is that they often suggest to a given user a list of POIs, which are specific tourist attractions that a user may find interesting such as a historical or cultural buildings or any places worth being visited, rather than a tour as an ordered set of POIs.…”

Section: Introductionmentioning

confidence: 99%

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

Bahramian

Abbaspour

Claramunt

2017

Mobile Information Systems

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Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user's feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning with -nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.

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“…The assignment of prescribing a customized touring visit is demonstrated as a case of the Summed up Most extreme Scope (GMC) issue, where a measure of individual enthusiasm for the client given her inclinations and going by time-spending plan is amplified. [5]It displays the improvement of the Traveller Guide, an area based vacation-er control application for the open air condition. Its concentrate for this venture is on programming support for area based applications; it is occupied with the area as well as different components of the client's specific circumstance.…”

Section: Literature Surveymentioning

confidence: 99%

Travel Package Recommendation System based on Package Locations and Rating

Dipali¹,

Pragati²,

Pratik³

et al. 2017

IJCA

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Throughout the decades, travelling has encountered constant development and extending expansion to wind up noticeably one of the quickest developing monetary segments on the planet. Among the current travelling applications, just a modest bunch encourage the capacity to design a visit which is totally in light of client inclinations, while offering a top to bottom take a gander at the coveted goal. Hence, this examination concentrates on coordinating semantic innovations, cooperative sifting into the area of travelling and give client arranged visit designs with superlative client fulfilment. Visit arranging and the method for investigating wanted courses, real stops or attractions en route by means of virtual reality 360 view understanding. Moreover, business associations can utilize the electronic dashboard to keep up their administrations, offers, bundles, spending report and acquire business diagnostic based changes.

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On planning sightseeing tours with TripBuilder

Cited by 106 publications

References 15 publications

User Transition Pattern Analysis for Travel Route Recommendation

User Transition Pattern Analysis for Travel Route Recommendation

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

Travel Package Recommendation System based on Package Locations and Rating

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