A Context-Aware Tourism Recommender System Based on a Spreading Activation Method

Bahramian, Zahra; Abbaspour, Rahim Ali; Claramunt, Christophe

doi:10.5194/isprs-archives-xlii-4-w4-333-2017

Cited by 14 publications

(9 citation statements)

References 27 publications

(23 reference statements)

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“…It means that from a predicted set of recommendations we select just those that match current user context. Bahramian et al [9] proposed a new context-aware tourism recommender system based on an ontology approach where a spreading activation technique is used to contextualize user preferences and learns the user profile dynamically. Negre et al [10] introduced a context-aware recommender system based on a contextual post-filtering for OLAP queries, where queries recommended by a classic log-based recommender system were contextualized.…”

Section: Related Workmentioning

confidence: 99%

Improving Re-rankCCP with Rules Quality Measures

Jezusek¹,

Karpus²

2022

Annals of Computer Science and Information Systems

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Recommender Systems are software tools and techniques which aim at suggesting new items that may possibly be of interest to a user. Context-Aware Recommender Systems exploit contextual information to provide more adequate recommendations. In this paper we described a modification of an existing contextual post-filtering algorithm which uses rules-like user representation called Contextual Conditional Preferences. We extended the algorithm by taking into account rules quality measures while recommending items to a user. We proved that this modification increases the quality of recommendations, measured with precision, recall and nDCG, and has no impact on the execution time of the original algorithm.

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Section: Related Workmentioning

confidence: 99%

Improving Re-rankCCP with Rules Quality Measures

Jezusek¹,

Karpus²

2022

Annals of Computer Science and Information Systems

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“…In this way, moving objects with similar frequencies in the velocitydistance waveform can be found not only in the known hot spots of the users, but also in the scenic spots that the potential users may be interested in, even in the preferences, occupations, and personality characteristics of the tourist users. It helps to gather tourists with similar preferences and similar personalities to achieve the confluence module [20].…”

Section: Discovery Of Popular Tourist Attractionsmentioning

confidence: 99%

Research on Precision Marketing Model of Tourism Industry Based on User’s Mobile Behavior Trajectory

Zhang

Fan

2019

Mobile Information Systems

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With the deep cross-border integration of tourism and big data, the personalized demand of tourist groups is increasingly strong. Precision marketing has become a new marketing mode that the tourism industry needs to pay close attention to and explore. Based on the advantages of big data platform and location-based service, starting from the precise marketing demand of tourism, we design data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment to get user track data that incorporates location information, consumption information, and social information. Data mining clustering technology is used to analyze the characteristics of users’ mobile behavior trajectories, and the precise recommendation system of tourism is constructed to provide support for tourism decision making. It can target the tourist group for precise marketing and make tourists travel smarter.

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“…Additionally, in this sense, an interesting problem is the one related to the problems of information overload. In this regard, in [24] a recommender system is proposed, which is able to provide customized information based on user preferences. This system is also able to embed contextual information and user preferences through ontological approaches that aim to improve the recommendation process.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Recommending Contextualized Services in e-Tourism

et al. 2019

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«You take delight not in a city’s seven or seventy wonders, but in the answer it gives to a question of yours». Those are the words used by Italo Calvino in his book “Invisible Cities” to give us a key aspect of a city, and it is from this consideration that this research work starts. In particular, the aim is to study and develop innovative solutions that guarantee value for the territory and for the cultural and landscape assets that insist on it. At the same time, such innovative solutions should be able to make the answers, connected to these resources, “tailored” to the user who requested it. For this reason, an architecture will be described, which allows content generating actors and users to operate by means of a broker-platform. Through this platform, it is possible to search a content within a Knowledge Base and, through the automatic orchestration of services, to activate in a controlled way the access and fruition of the information contained in it.

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A Context-Aware Tourism Recommender System Based on a Spreading Activation Method

Cited by 14 publications

References 27 publications

Improving Re-rankCCP with Rules Quality Measures

Improving Re-rankCCP with Rules Quality Measures

Research on Precision Marketing Model of Tourism Industry Based on User’s Mobile Behavior Trajectory

An Approach for Recommending Contextualized Services in e-Tourism

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