Researchers have developed a tourist recommendation system combining three algorithms to help travellers find destinations suited to their preferences, budgets, and travel routes. Details appear in the International Journal of Work Innovation and address a limitation of digital platforms that often prioritise popularity over practical considerations.
The new model integrates three computational techniques. First, K-means clustering, which groups data by similarity, categorises attractions by tourism potential. Second, the Floyd-Warshall algorithm, which calculates the shortest distances between destinations to optimise journeys. Third, the weighted product method ranks options against criteria, including facilities, cost, and safety, according to their relative importance.
By combining these methods, the team says they obtain more accurate and efficient recommendations than through the use of a standalone algorithm. The inclusion of destination potential, geographical distance, and practical requirements in the input means their system can improve the decision-making process while reducing travel time and costs.
The team is based in Indonesia, and the research is particularly relevant to this part of the world as a tourist destination. The archipelago consists of more than 17,000 islands which have diverse natural, cultural, and historical attractions; choosing between them as a holiday destination is thus a rather complicated choice. Indeed, the breadth of the Indonesian tourist offering can make choosing destinations near impossible, despite the growing importance of digital platforms in trip planning. The team suggests that their integrated recommendation system could support tourism digitalisation, offering businesses and policymakers a data-driven tool for improving travel planning and destination management.
Muqorobin, Fitria, T.N., Mutiasari, A.I. and Rochmatullah, M.R. (2026) ‘Development of optimal tourist destination recommendation model using clustering and shortest-distance digitalisation’, Int. J. Work Innovation, Vol. 7, No. 4, pp.522–542.
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