From Photos to Travel Itinerary: A Tourism Recommender System for Smart Tourism Destination

Figueredo, Mickael; Ribeiro, José; Cacho, Nélio; Thome, Antonio; Cacho, Andréa do Nascimento Barbosa; Lopes, Frederico; Araújo, Valéria de Fátima Chaves

doi:10.1109/bigdataservice.2018.00021

Cited by 49 publications

(33 citation statements)

References 22 publications

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“…(3) Figueredo et al (2018) proposed a solution that is able to detect implicit preferences based on images shared on social networks and recommended a combination of tourist attractions. To classify tourists and make the recommendation, new techniques such as convolutional neural networks and fuzzy logic are used.…”

Section: Shapers Of the Tourism Industrymentioning

confidence: 99%

Smartness and social networks as shapers of the tourism industry

Vargas‐Sánchez

Saltos

2019

WHATT

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Purpose The purpose of this study is to present the state-of-the-art scientific research in the intersection between smart tourism and social networks to better understand the role of the latter (such as Facebook and Twitter) in the development of the smart paradigm in tourism, e.g. how the relationship between smart tourism and social networks has been observed by authors, or the techniques and analytical tools that are most broadly used to take advantage of available data in social networks to feed a smart logic in the management of tourism companies and destinations. Design/methodology/approach A systematic search in scientific databases (such as Web of Science and Scopus) was performed, with December 2018 as a closing date. Once duplications were eliminated and the adequacy the outputs obtained verified properly, a total of 61 documents were considered for this study, most of which were journal articles. Findings The characterization of research in this area with both a bibliometric and bibliographic analysis. Practical implications The presentation of a detailed picture on the content of the research carried out in the abovementioned intersections: evolution over time; most prolific authors, institutions and countries; journals, conferences and publishers that have paid more attention to this topic; type of research performed, objects of study, methodologies used and main contributions. Originality/value The proposal of a research agenda in the field under study. To the best of the authors’ knowledge, there is no a similar work carried out in this field.

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Section: Shapers Of the Tourism Industrymentioning

confidence: 99%

Smartness and social networks as shapers of the tourism industry

Vargas‐Sánchez

Saltos

2019

WHATT

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“…The final topic discussion is about Recommender Systems according to the selected papers: [7], [41], [42], [43], and [44]. In [7], it is offered a travel recommender system for the effects of automating Word-of-Mouth (WOM) and established personalized travel-planning services to tourists through Collaborative Filtering (CF)-based recommender using WOM communication.…”

Section: Recommender Systemsmentioning

confidence: 99%

Smart Tourism Recommendation Model: A Systematic Literature Review

Huda¹,

Ramadhan²,

Trisetyarso³

et al. 2021

IJACSA

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The tourism industry has become a potential sector to leverage economic growth. Many attractions are detected on several platforms. Machine learning and data mining are some potential technologies to improve the service of tourism by providing recommendations for a specific attraction for tourists according to their location and profile. This research applied for a systematic literature review on tourism, digital tourism, smart tourism, and recommender system in tourism. This research aims to evaluate the most relevant and accurate techniques in tourism that focused on recommendations or similar efforts. Several research questions were defined and translated into search strings. The result of this research was promoting 41 research that discussed tourism, digital tourism, smart tourism, and recommender systems. All of the literature was reviewed on some aspects, in example the problem addressed, methodology used, data used, strength, and the limitation that can be an opportunity for improvement in future research. This study proposed some references for further study based on reviewed papers regarding tourism management, tourist experience, tourist motivation, and tourist recommendation system. The opportunities for a further research study can be conducted with more data usage especially for a smart recommender system in tourism through many types of recommendation techniques such as content-based, collaborative filtering, demographic, knowledge-based, community-based, and hybrid recommender systems.

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“…Jiang et al [13] proposed a personalized travel sequence recommendation system by analyzing travelogues and community-contributed photos. Figueredo et al [14] designed a tourism recommender system based on machine learning algorithms to detect tourist implicit preferences based on social media photos and recommend tourism attractions. In addition, Majid et al [15] proposed a method for recommending locations for travelers with similar travel contexts based on community-contributed geotagged photos.…”

Section: Related Workmentioning

confidence: 99%

Toward the Design of Smart Travel Guard

Kao

2019

Smart Science

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Nowadays, travelers usually search, collect, identify, and preserve valuable travel information from large amounts of online data sources before the trip. Meanwhile, during the trip, travelers can use smart devices to seek help according to particular circumstances. However, thefts, robberies, and scams always happen during the trip. One of the major issues is that travelers cannot collect all of the related travel information and identify important messages efficiently. In addition, the assistance provided by smart devices is not timely enough. Thus, in order to protect travelers from potential risks or dangers, a framework named 'Smart Travel Guard' is proposed in this paper. The smart travel guard helps to continuously collect, identify, classify, and annotate travel information from different online data sources. The related travel context and travel information can be identified, classified, and preserved, and a safety index for a travel attraction can be calculated and summarized. The smart travel guard also acquires particular travel information from smart devices carried or wore by travelers in real time. The acquired information will be recognized and analyzed together with the existing travel information and used to retrieve the historical safety index. Based on the analysis result, the corresponding message based on the safety index can be generated, and associated travel information can be provided for travelers to prevent potential risks or dangers in advance. After the assistance, the confirmation and feedback from travelers will be collected and preserved. Thus, the information can be accumulated in the smart travel guard for further reference. It is anticipated that the proposed smart travel guard can complement current recommendation systems and achieve quality and safe travel effectively and efficiently.

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From Photos to Travel Itinerary: A Tourism Recommender System for Smart Tourism Destination

Cited by 49 publications

References 22 publications

Smartness and social networks as shapers of the tourism industry

Smartness and social networks as shapers of the tourism industry

Smart Tourism Recommendation Model: A Systematic Literature Review

Toward the Design of Smart Travel Guard

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