A Comprehensive Survey on Travel Recommender Systems

Chaudhari, Kinjal; Thakkar, Ankit

doi:10.1007/s11831-019-09363-7

Cited by 57 publications

(21 citation statements)

References 134 publications

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“…Therefore, transport is a process of great importance for the accessibility of the POIs and to define to some degree the attractiveness of these sites (Le-Klähn & Hall, 2015 ; Van Truong & Shimizu, 2017 ). Transport is one of the sectors responsible for the operation and development of tourism (Chaudhari & Thakkar, 2019 ). In addition, transport is included in the TTPD for the flow of tourists (Gavalas et al, 2015a ) considering travel time, transport mode, among other aspects that can be associated with transport (Garcia et al, 2009 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Ruiz-Meza

Montoya‐Torres

2021

Ann Oper Res

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Tourism is one of the fastest-growing sectors in the world with a shift from mass tourism to personalized travel. Nevertheless, it generates significant environmental impacts. The current events associated with quarantine measures generated by COVID-19 represent, however, a risk for this sector. It is hence necessary to create strategies that allow efficient decision-making for all echelons and actors for a rapid recovery. Tourists are key actors, which makes necessary to facilitate tourism trip planning according to tourists’ preferences as a complex process. In this paper, we propose a novel model of tourist trip planning for heterogeneous preferences in a tourist group and selection of transport modes, in the first instance, while a second step seeks at minimizing the level of CO 2 emissions. A comparison of the two models is made considering the objectives associated with individual tourist benefits and group profit equity, in contrast to the inclusion of the cost of CO 2 emissions. A numerical comparison is carried out with a total of 546 data sets. Results illustrate the conflict between those objectives by generating an inverse relationship between the individual and group profit equity of tourists, in addition to individual benefit and emission minimization.

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Section: Literature Reviewmentioning

confidence: 99%

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Ruiz-Meza

Montoya‐Torres

2021

Ann Oper Res

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“…This context is especially applicable to the domain of flight bookings, which is both prediction (i.e., going to a prior destination) and recommendation (i.e., going to a new destination), combined with continual upheaval in how potential travelers make bookings [ 47 ]. Prior research using historical data investigated e-commerce recommendations [ 31 ] and reported that various factors [ 5 ] should be considered in tandem with recommendations [ 10 ]. However, the researchers [ 31 ] did not have access to actual revenue data or the specific deployed algorithm.…”

Section: Prior Workmentioning

confidence: 99%

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

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Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.

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“…Individual tourism is a challenging domain for recommender systems due to the substantial complexities of planning an independent trip and the huge economic importance of the travel and tourism industry (Chaudhari and Thakkar 2019). Big commercial player such as Booking, Tripadvisor, and Skyscanner focus on recommending single items, such as hotels, restaurants, and flights.…”

Section: Tourist Recommender Systemsmentioning

confidence: 99%

Mining trips from location-based social networks for clustering travelers and destinations

Dietz

Sen

Wörndl

2020

Inf Technol Tourism

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It is important to learn the characteristics of travelers and touristic regions when trying to generate recommendations for destinations to users. In this work, we first present a data-driven method to mine trips from location-based social networks to understand how tourists travel the world. These trips are quantified using a number of metrics to capture the underlying mobility patterns. We then present two applications that utilize the mined trips. The first one is an approach for clustering travelers in two case studies, one of Twitter and another of Foursquare, where the pure mobility metrics are enriched with social aspects, i.e., the kinds of venues into which the users checked-in. Clustering 133,614 trips from Twitter, we obtain three distinct clusters. In the Foursquare data set, however, six clusters can be determined. The second application area is the spatial clustering of destinations around the world. These discovered regions are solely formed by the mobility patterns of the trips and are, thus, independent of administrative regions such as countries. We identify 942 regions as destinations that can be directly used as a region model of a destination recommender system. This paper is the extended version of the conference article "Characterisation of Traveller Types Using Check-in Data from Location-Based Social Networks" presented at the

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A Comprehensive Survey on Travel Recommender Systems

Cited by 57 publications

References 134 publications

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Mining trips from location-based social networks for clustering travelers and destinations

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