Measuring flight-destination similarity: A multidimensional approach

Anat Goldstein, Chen Hajaj

Research output: Contribution to journalArticlepeer-review


E-tourism websites offer users a vast array of travel destinations and opportunities, necessitating tools that enable destination comparison and intelligent search capabilities. One key requirement for such tools is the ability to measure the similarity between destinations. Over the years, various similarity measurement techniques have been proposed, including user-based and content-based approaches. However, many of these techniques require data preparation or prior domain knowledge from experts. In contrast, this study proposes an innovative approach that requires no prior domain knowledge of flight destinations or their relationships, and utilizes only readily available data. Our approach draws upon concepts from image recognition and natural language processing (NLP) to extract hidden aspects of destinations. Using data from a flight-search website as a testbed, we analyze similarity metrics based on state-of-the-art methods for image recognition, NLP, and product-network analysis. We then compare these metrics to those obtained by human subjects. Our findings suggest that no single method dominates in all aspects, leading us to propose a hybrid method that leverages the strengths of each. The proposed method can be readily applied to measure product similarity in other domains.

Original languageEnglish
Article number121802
JournalExpert Systems with Applications
StatePublished - 15 Mar 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd


Funding: This work was supported by the Data Science and Artificial Intelligence Research Center, Ariel University, Israel [grant number RA2200000229 ].

FundersFunder number
Ariel University, IsraelRA2200000229
Data Science and Artificial Intelligence Research Centre, Nanyang Technological University


    • E-commerce
    • E-tourism
    • Image-Embeddings
    • Multi-Modal Product Representation
    • Product Network
    • Product Similarity
    • Text-Embeddings


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