Tourist Trajectory Data Warehouse: Event Time of Interest, Region of Interest and Place of Interest

Hilali, Intissar; Arfaoui, Nouha; Ejbali, Ridha

doi:10.1007/978-3-031-35507-3_33

Cited by 1 publication

(2 citation statements)

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“…To obtain historical data about tourists, we use our Trajectory Data Warehouse, which stores data on past itineraries, visited locations, preferences, and other relevant details. The purpose of this data warehouse is to handle massive amounts of trajectory data in an organized and effective manner [38]. Historical data can be accessed by executing specific queries on the database.…”

Section: Previous History Of the Visitmentioning

confidence: 99%

“…In this study, we have four locations. These locations are represented by ROI Gabes.The second dataset contains data about the historic Trajectory Data warehouse [38], which is already the history of the ancient tourists.The third dataset consists of images from the UTK face 1 data of people divided into different age groups. These datasets were employed as multi-datasets to evaluate the performance of our system recommendations.…”

Section: Datasetmentioning

confidence: 99%

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TourOptiGuide: A Hybrid and Personalized Tourism Recommendation System

Intissar,

Nouha,

Ridha

2024

Preprint

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When visitors explore a city briefly, they must prioritize the key attractions that align with their interests. These significant points of interest (POIs) can be chosen based on specific criteria tailored to their needs. Additionally, travellers venturing into unfamiliar regions often seek help to plan their itinerary. To address this issue, we developed and presented a novel hybrid and personalized recommendation system aimed at helping tourists choose their next POI. The system tailors its suggestions based on four key factors: the tourist's current location, single preferences, age, and historical experiences. Deep learning models play a crucial role in identifying the tourist's current location from images and predicting age from selfies. In addition, our system leverages a trajectory data warehouse containing extensive historical data of past tourist’s experiences to provide suggestions. The core of our recommendation strategy is a fuzzy logic decision support system. This system effectively synthesizes diverse inputs to produce the top next POI to visit. By integrating various recommendation methods, our hybrid system significantly improves the precision and pertinence of its recommendations, offering a more customized and effective travel experience. Preliminary results demonstrate significant improvements in tourist satisfaction and in the efficiency of itinerary planning.

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Section: Previous History Of the Visitmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%