Cruise tourism can be easily characterized as an “experienced” generator industry, noting that the services provided by the cruise liners have an experiential core while their consumption aims to deliver certain emotional outcomes. The cruise experience comprises two components: the onboard experience and the onshore.
The objective of the “Destination Challenge” is to enhance cruise passengers’ onshore experience by providing a blending of cultural activities and local production-consumption activities, linked through a treasure hunt game.
A simple map-based app will challenge cruise visitors to experience the destination while benefiting from the special offers and discounts known as “crusemons”.
Destination Challenge: What problem are we trying to solve?
Cruise tourism expenditures in destination ports are composed of a broad range of spending including:
- Passengers’ onshore expenditures consist of shore excursions, retail purchases of clothing and jewellery, and food and beverage purchases.
- The crews’ onshore expenditures consist of food and beverage purchases in restaurants, and retail purchases of jewellery and clothing;
- The cruise lines’ expenditures for port services – such as dockage fees and linesmen, utilities – such as water, power and navigation services; and
- Purchases of supplies – such as food, and other supplies from local businesses. To this extent, the main challenge addressed is to increase the amount cruise passengers spend on local businesses by developing experiences.
To this extent, the main challenge addressed is to increase the amount cruise passengers spend on local businesses by developing experiences.
f society’s technology is based on Recommender Systems. These systems consist of tools for data analysis that match items with users and their specific needs. This emerging scientific field, which is less than twenty years old, is constantly growing with numerous new techniques and applications. Destination challenge takes advantage of our own graph-based algorithm for recommendations.
How do we achieve it? Let us explain how: maths. Firstly, we examine some notable results from node ranking and random walks on graphs. Then, we utilize the notion of functional rankings and next comes the Multi-Damping framework for ranking. The framework used considers several aspects for more complete manipulation of the data. Then, the approach is graph-based. Random walks with restarts produce rankings, whereby the spectral properties of the transition probability matrix determine the number of steps. In each step, the focus is on the closest neighbors and a re-ranking step, which offers diversity, takes place at the end