Recommendation and Planning through Mobile Devices in Tourism Context

Anacleto, Ricardo; Figueiredo, Lino; Luz, Nuno; Almeida, Ana; Nováis, Paulo

doi:10.1007/978-3-642-19937-0_17

Cited by 8 publications

(9 citation statements)

References 3 publications

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“…In terms of recommendation algorithms, it applies content-based filters which use the weather conditions, time, schedule of POI, etc. to produce personalised recommendations (Anacleto et al ., 2011).…”

Section: Cata-related Workmentioning

confidence: 99%

“…This complementary data are typically used to enrich the user experience and for further personalisation: Sensor Services are used in CATA to obtain in real or deferred time environmental information, for example, weather. GuideMe (Umanets et al ., 2014), VISIT (Meehan et al ., 2013), ReRex (Baltrunas et al ., 2011), liveCities (Martin et al ., 2011) and PSiS (Anacleto et al ., 2011) rely on remote sensor services to gather meteorological information. Social Platforms expose user generated content known as crowdsourced data. Social networks and tourism crowdsourcing platforms are popular for sharing tourism experiences.…”

Section: Cata: Knowledge Acquisition Modulementioning

confidence: 99%

“…Sensor Services are used in CATA to obtain in real or deferred time environmental information, for example, weather. GuideMe (Umanets et al ., 2014), VISIT (Meehan et al ., 2013), ReRex (Baltrunas et al ., 2011), liveCities (Martin et al ., 2011) and PSiS (Anacleto et al ., 2011) rely on remote sensor services to gather meteorological information.…”

Section: Cata: Knowledge Acquisition Modulementioning

confidence: 99%

“…cosine similarity). MAELT (Silva et al ., 2018), SPLIS (Viktoratos et al ., 2015, 2014), Trip@cloud (Qureshi et al ., 2011), Loc-based App (Panahi et al ., 2013), Trip Planning (Herath & Ratnayake, 2013), GeOasis (Santiago et al ., 2012), ReRex (Baltrunas et al ., 2011), liveCities (Martin et al ., 2011), PSiS (Anacleto et al ., 2011), PaTac (Ceccaroni et al ., 2009) and SAMAP (Castillo et al ., 2008) use a content-based filter to produce the recommendations.…”

Section: Cata: Knowledge Processing Modulementioning

confidence: 99%

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Context-aware tourism technologies

Leal¹,

Malheiro

Burguillo³

2018

The Knowledge Engineering Review

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Nowadays travellers can benefit from the computing capabilities, collection of on board sensors and ubiquitous Internet access provided by mobile devices. These are the three pillars of any tourist support system since they provide the power, means and data to establish the local user context, to access remote services and to provide value-added user-centred context-aware applications. However, making sense of the user context data is not straightforward, as it requires dedicated knowledge acquisition and knowledge representation solutions. Besides, the range and diversity of available data sources is huge, requiring appropriate knowledge processing techniques to provide addequated tourism services. This article presents an updated review, and a comparison of recent context-aware tourism applications (CATA), including supporting technologies; and considering four possible dimensions: knowledge acquisition, knowledge representation, knowledge processing and knowledge-based services. We propose and apply a CATA analysis framework, contemplating these four dimensions to the applications found in the literature. This survey constitutes, not only, a state of the art review on tourism mobile applications, but, also, anticipates the latest development trends in tourism-related applications.

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Section: Cata-related Workmentioning

confidence: 99%

Section: Cata: Knowledge Acquisition Modulementioning

confidence: 99%

Section: Cata: Knowledge Acquisition Modulementioning

confidence: 99%

Section: Cata: Knowledge Processing Modulementioning

confidence: 99%

See 2 more Smart Citations

Context-aware tourism technologies

Leal¹,

Malheiro

Burguillo³

2018

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…A more extended state of the art can be seen in some of our previous works (Anacleto et al, 2010(Anacleto et al, , 2011.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile application to provide personalized sightseeing tours

Anacleto¹,

Figueiredo²,

Almeida³

et al. 2014

Journal of Network and Computer Applications

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a b s t r a c tTourist recommendation systems have been growing over the last few years, mainly because of the use of mobile devices to obtain user context. This work discusses some of the most relevant systems on the field and presents PSiS Mobile, which is a mobile recommendation and planning application designed to support a tourist during his vacations. It provides recommendations about points of interest to visit based on tourist preferences and on user and sight context. Also, it suggests a visit planning which can be dynamically adapted based on current user and sight context. This tool works also like a journey dairy since it records the tourist moves and tasks to help him remember how the trip was like. To conclude, some field experiences will be presented.

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