A Personalized Hybrid Tourism Recommender System

Kbaier, Mohamed Elyes Ben Haj; Masri, Hatem; Krichen, Saoussen

doi:10.1109/aiccsa.2017.12

Cited by 57 publications

(39 citation statements)

References 16 publications

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“…This tends to suggest the need to develop community and cultural tourism. Cultural tourism entails interacting with the local people in order to understand their history, present and future [95].…”

Section: Not Significant (Crmentioning

confidence: 99%

Development of a Destination Image Recovery Model for Enhancing the Performance of the Tourism Sector in the Developing World

Kanokanga¹,

Tukuta²,

Chikuta³

2021

Tourism

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This chapter is based on a doctoral thesis on the development of a destination image (DI) recovery model for enhancing the performance of the tourism sector in Zimbabwe. The study was prompted by the failure of African destinations to develop DI image recovery models. A pragmatist paradigm, a convergent parallel mixed methodology research approach and a cross sectional survey were adopted. A sample of three hundred and nineteen comprising international tourists, service providers and key informants was used. A structured, semi-structured questionnaire and semi-structured interview guide were used respectively. Quantitative data was analyzed using the Statistical Package for Social Sciences (SPSS) and AMOS version 25 while qualitative data was analyzed using NVivo version 12. Tests were conducted using descriptive statistics, exploratory factor analysis, and confirmatory factor analysis. Structural Equation Modeling (SEM) was used to analyze the multiple independent variables. The major findings were that price, ancillary services and amenities significantly influenced affective image while ancillary services significantly influenced destination performance. The study recommended that the Ministry of Environment, Climate, Tourism and Hospitality Industry trains tourism stakeholders including the host community in order to achieve sustainable destination image recovery.

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Section: Not Significant (Crmentioning

confidence: 99%

Development of a Destination Image Recovery Model for Enhancing the Performance of the Tourism Sector in the Developing World

Kanokanga¹,

Tukuta²,

Chikuta³

2021

Tourism

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“…In addition, most people usually travel less frequently than e.g., consuming music or movies, making collaborative recommendations even less reliable due to the cold start problem. Instead, contentbased and knowledge-based recommendation techniques are often employed (Burke and Ramezani 2011), or in case it is possible, hybridization (Kbaier et al 2017).…”

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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“…Lee et al [19] used a semantic approach to measure the similarity using the location. Kbaier et al [20] proposed a hybrid method combining three machine-learning algorithms to form a recommendation system. At first, they used the K-nearest neighbor (KNN) clustering for a collaborative filtering module.…”

Section: Place Recommendation Systemmentioning

confidence: 99%

An Empirical Recommendation Framework to Support Location-Based Services

et al. 2020

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The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users.

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A Personalized Hybrid Tourism Recommender System

Cited by 57 publications

References 16 publications

Development of a Destination Image Recovery Model for Enhancing the Performance of the Tourism Sector in the Developing World

Development of a Destination Image Recovery Model for Enhancing the Performance of the Tourism Sector in the Developing World

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

An Empirical Recommendation Framework to Support Location-Based Services

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